---

Join Our Workgroup   GitHub Repo

Unlocking AI Potential: Navigating the Challenges and Opportunities of Diverse Hardware Accelerators

David Edelsohn, AI Alliance, IBM
Andrew Richards, UXL Foundation
Michael Wong, UXL Foundation, Khronos, ISOCPP, RISC-V
Thanks to AMD, Intel, Meta, and Modular

Introduction

The artificial intelligence (AI) landscape is experiencing an unprecedented surge in innovation, particularly in the realm of AI accelerator hardware. These accelerators, designed to significantly speed up AI computations, are the engines behind today’s AI breakthroughs, powering everything from natural language processing to computer vision tasks, generating songs or movies to driving cars. However, the rapid proliferation of AI hardware accelerators, each with its own unique architecture and capabilities, has given rise to a complex ecosystem. This complexity is further magnified by the diversity of AI frameworks, such as PyTorch, JAX, TensorFlow, PaddlePaddle, Pallas, and Triton, each offering distinct advantages and designed with specific use cases in mind.

The convergence of cutting-edge AI hardware and sophisticated software frameworks is where the true potential of AI is unleashed. It is at this intersection where groundbreaking advancements are made, enabling the development of more powerful, efficient, and intelligent AI systems. However, this junction also presents significant challenges for developers and researchers, who must navigate the intricate web of compatibility, performance optimization, and integration issues that arise when working with disparate hardware and software components.

This comprehensive review aims to demystify the complexities of the AI ecosystem, providing a clear and in-depth exploration of the interactions between various AI accelerators and frameworks. By examining the challenges posed by this rapidly evolving landscape and presenting strategies to effectively navigate its intricacies, this document serves as an invaluable resource for developers, researchers, and decision-makers alike. Whether you are a seasoned AI practitioner or a newcomer to the field, this guide will equip you with the knowledge and insights needed to harness the full potential of AI hardware and software, driving innovation and progress in this transformative domain.

Motivation

The increasing use of Artificial Intelligence, Deep Learning, Foundation Models, and Machine Learning throughout a wide range of tasks drives a wide range of requirements from both hardware and software. At one end of the spectrum, the training of large-scale models for natural language processing, self-driving vehicles, and generative AI demands immense computational resources. These tasks often require clusters of specialized hardware to process the vast number of calculations within a reasonable timeframe. On the other hand, the inference of these models, while less computationally intensive compared to training, can still pose challenges when deployed at scale, requiring a different mix of hardware and software optimizations to ensure efficient and responsive performance.

Moving along the spectrum, simpler AI models designed for applications such as fraud detection may have lower computational demands but often come with strict latency and throughput constraints. As we shift towards the edge of the network, AI deployments on self-driving cars, mobile devices, and smart appliances introduce additional requirements for power efficiency and real-time processing. To address these challenges, specialized AI chips with lower power consumption and dedicated AI processing capabilities have emerged as crucial components in edge computing scenarios.

At the extreme end of the spectrum, TinyML focuses on running AI models on devices with severely limited resources, such as wearables and sensor nodes. In these cases, specialized microcontrollers with built-in AI capabilities play a vital role in enabling basic AI tasks while maintaining minimal power consumption and form factor.

The challenge for vendors, software developers, and AI application developers lies in developing and utilizing common frameworks that can scale AI models across this diverse range of use cases, deployment environments, and hardware platforms. Adaptability is key for AI frameworks to effectively leverage the unique capabilities of each AI accelerator and hardware configuration, ultimately maximizing performance and efficiency for every AI application.

This guide aims to explore the strategies and solutions for navigating these complexities.

Introduction to AI Hardware Accelerators

AI accelerators have become pivotal in the modern AI landscape due to their specialized computational capabilities, designed to speed up artificial intelligence (AI) applications, including deep learning, machine learning, and data processing tasks. These accelerators perform numerically intensive computations at an unprecedented scale, allowing for rapid training and inference phases of AI models. The significance of AI accelerators lies in their ability to handle vast amounts of data and complex computations efficiently, reducing the time and energy consumption associated with traditional computing methods. This optimization unlocks new possibilities in AI development, enabling more sophisticated and accurate AI models, which are essential for advancing technologies in fields such as autonomous vehicles, healthcare, finance, entertainment, and natural language processing.

In the realm of AI accelerator hardware, key players include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), NPUs (Neural Processing Units), Field-Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs), and Application-Specific Integrated Circuits (ASICs). GPUs, originally designed for rendering graphics, have been widely adopted for AI due to their high throughput and ability to handle parallel tasks. TPUs, developed by Google, are specifically tailored for TensorFlow operations, offering optimized performance for deep learning tasks. NPUs are specialized for neural network computations, often embedded in smartphones and IoT devices for on-device AI. FPGAs present a flexible architecture, allowing for customization to specific computational needs, making them suitable for prototyping and adaptive algorithms. ASICs are custom-designed for a particular use case, offering the highest efficiency and performance for specific AI tasks, but lack the versatility of GPUs and FPGAs.

AI accelerators play a crucial role in enhancing the performance of AI models by significantly reducing computation time and increasing efficiency. This improvement is critical for training complex models with billions of parameters, a common requirement in today’s AI challenges. By offloading heavy computational tasks to accelerators, developers can achieve higher throughput and lower latency in both training and inference phases. This enables more iterative experimentation, quicker model development, and the deployment of more advanced AI applications across various industries. Essentially, AI accelerators are the backbone that supports the rapid growth and scalability of AI technologies.

AI frameworks are the software foundations driving innovation in the AI field, providing developers with the tools and libraries needed to design, train, and deploy AI models. Leading AI frameworks include PyTorch, JAX, TensorFlow, Keras, Apache MXNet, Caffe, Chainer, Theano, Microsoft Cognitive Toolkit (CNTK), DL4J, PaddlePaddle, MindSpore, Pallas, and Triton. PyTorch, known for its dynamic computational graph and user-friendly interface, is favored for research and prototyping. JAX excels in high-performance numerical computing and machine learning research with its automatic differentiation and GPU/TPU acceleration. TensorFlow, developed by Google, offers a comprehensive ecosystem for developing and training ML models at scale, with strong support for TPUs and deployment. Keras provides a high-level API for building and training deep learning models and acts as an interface for TensorFlow. Apache MXNet offers flexibility and efficiency for deep learning. Caffe, developed by the Berkeley Vision and Learning Center, is known for its speed and modularity. Chainer is praised for its flexibility in enabling fast implementation of research ideas. Theano provides efficient definition, optimization, and evaluation of mathematical expressions. Microsoft Cognitive Toolkit (CNTK) supports commercial-grade distributed deep learning. DL4J, designed for business environments, supports distributed GPUs and CPUs. PaddlePaddle, developed by Baidu, emphasizes ease of use, scalability, and efficiency. MindSpore, by Huawei, offers comprehensive AI development and deployment capabilities. Pallas focuses on high-performance computing on GPUs, emphasizing efficiency and speed. Triton simplifies writing highly efficient GPU code, democratizing access to custom high-performance computations.

Each framework offers unique features and caters to different use cases. PyTorch is renowned for its flexibility and ease of use, making it ideal for academics and researchers focused on developing novel AI models. JAX’s ability to automatically differentiate through Python and NumPy code is particularly useful for scientists and researchers working on complex simulations and models. TensorFlow’s extensive community and robust tooling make it suitable for industrial applications requiring scalability and production readiness. Pallas, with its focus on maximizing GPU utilization, is well-suited for high-performance tasks that require extreme computational efficiency. Triton, offering an approachable way to write custom GPU kernels, appeals to developers needing to optimize specific operations beyond what is available in standard libraries.

AI frameworks are rapidly evolving to leverage the diverse capabilities of various hardware accelerators, ensuring that the computational power of GPUs, TPUs, FPGAs, and ASICs can be fully harnessed for AI development. This adaptation involves the integration of specialized libraries and APIs that facilitate direct communication between the software and the underlying hardware, optimizing for performance and efficiency. For instance, TensorFlow and PyTorch have introduced support for TPUs and CUDA-enabled GPUs, allowing developers to more easily shift workloads to these accelerators for faster processing. Additionally, frameworks are incorporating features like automatic mixed precision (AMP) and graph optimization techniques to further enhance computational efficiency on accelerators. The development of hardware-agnostic interfaces, such as ONNX (Open Neural Network Exchange), also plays a crucial role in this adaptation, enabling models trained in one framework to be executed on different types of accelerators. By embracing these advancements, AI frameworks not only unlock the potential of existing hardware but also pave the way for the next generation of AI innovations, ensuring that developers can focus on creating cutting-edge models without being constrained by hardware compatibility issues.

The Complexity of the Ecosystem

The available paths through the AI software ecosystem from model to hardware are very fragmented and parochial. Models are evolving rapidly, frameworks are evolving rapidly, hardware is evolving rapidly, which makes it difficult to ensure interoperability among the complex matrix of options. New innovations from various quarters of the AI industry are addressing pieces of the problem and exposing an opportunity to stitch together a comprehensive solution.

The primary AI frameworks for large language models are PyTorch, originally developed by Meta, and TensorFlow and JAX, developed and sponsored by Google. The frameworks have developed their own infrastructure for deploying operators on hardware as well as third-party infrastructure, such as ONNX and TVM.

PyTorch: A Detailed Exploration

PyTorch provides two primary modes of operation: Eager Mode and Graph Mode.

• Eager Mode can be considered an interpreted mode in which each operator in the model is executed as it is encountered. This provides flexibility and ease of debugging.
• Graph Mode constructs a graph from the operators, permitting various forms of optimization before executing the transformed set of operators as a whole. PyTorch Graph Mode has evolved through multiple iterations of compilers, including TorchScript and the more recent Dynamo.

PyTorch also has evolved through multiple iterations to transmit operators to hardware. PyTorch Eager mode can invoke operator kernels directly, such as those written directly in NVIDIA CUDA, AMD HIP, or Khronos SYCL, all similar C++-based kernel languages. PyTorch Eager Mode invocation of CUDA C++ can be translated to AMD HIP C++ invocation of ROCm through use of HIPify tools. PyTorch can utilize NVIDIA CUDA kernels produced by the Python-based OpenAI Triton language, which also is gaining support to directly generate AMD ROCm code. PyTorch Eager Mode operators can be captured by CUDAGraph for optimization of invocation of kernels. PyTorch also is able to leverage oneDNN to access processor-specific kernels and operators, to complement the pervasive NVIDIA cuDNN kernels and the expanding AMD ZenDNN kernels. PyTorch continues to maintain a path to the OpenBLAS library to expand its breadth of CPU targets.

PyTorch Graph mode currently utilizes the Dynamo compiler, which has enhanced the compilation process with the concept of graph breaks to include assertions in the compiled graph that allow transparent fallback to Eager Mode when characteristics of the model dynamically change. Dynamo leverages the CPython Frame API to analyze the function at runtime and identify potential optimizations, including recognizing PyTorch operations. This allows for handling more dynamic Python features like conditionals, loops, and dynamic control flow, but might have slightly higher overhead compared to JAX for simple static functions. Dynamo feeds its optimized graph to the PyTorch Inductor compiler for hardware target specific transformation. OpenAI Triton primarily is intended as a high-level human-written language for AI model kernels; Inductor targets Triton as an intermediate representation to generate CUDA and ROCm code directly, in addition to the ability to invoke pre-existing kernels (either hand-written or parametrically generated).

Microsoft, Meta and others are cooperating on Triton-Shared – an ambitious effort to utilize the Triton Language for non-GPU hardware accelerators, such as Microsoft Maia and Meta MTIA accelerator processors. Triton-Shared utilizes the LLVM MLIR project, particularly targeting the LinAlg and MemRef dialects to transform and optimize the Triton kernels, or potentially multi-kernel sub-graphs, for NPU-like hardware accelerators. Triton-Shared ingests Triton kernels, which means that the IR already has been lowered and some of the context and semantic information lost relative to PyTorch Graphs, limiting some of the optimizations available in MLIR dialects.

Beyond PyTorch: The Broader Landscape

IREE-Turbine is an effort to ingest PyTorch FX/Dynamic graphs into the IREE pipeline through the Torch-MLIR dialect, leveraging the entire IREE optimization infrastructure described in more detail in a later section. The Torch-MLIR dialect serves as a bridge, translating PyTorch models into an intermediate representation compatible with the MLIR ecosystem. Torch-MLIR continues to expand its support for a widening variety of models, including improvements for the important feature of dynamic tensor shapes. The translation facilitates the application of various optimizations and transformations inherent to MLIR, ensuring efficient execution across different hardware backends. This integrated approach streamlines the deployment of PyTorch models, enhancing their portability and efficiency across a wide range of AI accelerators, thereby accelerating the development and deployment of AI applications.

PyTorch can also utilize Google’s XLA infrastructure to leverage its optimization framework and to target XLA-based accelerators, such as Google TPU. XLA will be discussed in the TensorFlow and JAX section.

Other projects have tied PyTorch Graphs into MLIR, either directly or via ONNX. The lack of a formal specification for MLIR dialects and the instability of the MLIR and LLVM APIs complicate the ability to utilize MLIR directly. Triton and IREE offer the potential of a stable API interface for AI/ML compiler passes utilizing MLIR.

While MLIR provides a path to leverage PyTorch Graph Mode on diverse hardware, PyTorch Eager Mode remains widely used and more strongly tied to GPUs and DNN libraries.

TensorFlow, JAX and Pallas: Building for Diverse Hardware

TensorFlow and JAX have created an extensive infrastructure in support of diverse hardware. Google has developed the OpenXLA compiler ecosystem to support diverse accelerator hardware.

XLA Compiler

The XLA compiler optimizes linear algebra computations for CPUs, GPUs and ML Accelerators. JAX utilizes a tracer to convert Python functions to its own, internal representation, which is sent to XLA for compilation. This requires functions to be mostly pure (no side effects) and have static shapes for optimal results. It compiles the function once for specific input shapes and types. Subsequent calls with the same shapes reuse the compiled version, leading to speedups. However, changing input shapes triggers recompilation. Similar to PyTorch, the TensorFlow/JAX design utilizes XLA to optimize and tie together tensor operations in conjunction with calls to hand-optimized tensor kernel libraries for critical performance operations.

Kernel Optimization

TensorFlow and JAX adopted Eigen as an early design choice to optimize kernels for a wide breadth of CPU targets. They have added oneAPI oneDNN as a path to invoke optimized kernels and operators for x86_64 CPUs AVX, ARM CPUs SVE, and Intel GPUs, to complement support for NVIDIA cuDNN and AMD ZenDNN kernels.

Pallas extension and Custom Kernel Development

JAX has added the Pallas extension to write custom kernels for GPUs and TPUs. Pallas utilizes a code generation path through Mosaic for TPUs and through Triton for GPUs.This enables developers to write high-performance custom kernels without needing to delve into the intricacies of each hardware platform.

The combination of TensorFlow, JAX, and Pallas creates a powerful ecosystem for accelerating machine learning workloads across diverse hardware. By leveraging the optimization capabilities of XLA, the performance benefits of hand-optimized kernel libraries, and the flexibility of custom kernel development through Pallas, developers can harness the full potential of their hardware accelerators while maintaining a high level of productivity and portability.

PaddlePaddle: Designed for Ultra-Large-Scale AI

PaddlePaddle has developed a high performance software stack that flexibly targets a wide variety of workloads. Its design has been optimized for ultra large scale neural network training.

Compiler Infrastructure

PaddlePaddle utilizes its own compiler infrastructure, which includes importing the model either in native Paddle format or X2Paddle conversion from TensorFlow, Caffe, or ONNX. High-level optimizations are applied in the HLIR representation that is close to the original model. HLIR captures the high-level structure and semantics of the model, including the computation graph and operators. It is used to perform high-level optimizations, such as operator fusion and algebraic simplifications, before translating the model into Paddle IR (PIR). PIR abstracts the details of the model, making it easier to apply intermediate-level optimizations, such as inlining, dead code elimination, and loop transformations. PIR serves as a bridge between the high-level model definitions and the lower-level optimizations performed by Compiler Infrastructure for Neural Networks (CINN), which compiles it into executable code for specific hardware platforms. CINN applies hardware-specific optimizations, manages memory allocation, and ensures efficient data movement to maximize performance.

Compiler Backend

Paddle utilizes Eigen for optimized tensor operations. It also can utilize oneDNN as a backend, alongside GPUs (CUDA, ROCm) and XPU, and optionally OpenBLAS for BLAS operations on CPUs. For specialized hardware, PaddlePaddle offers support for XPU, Baidu’s custom-designed accelerator for deep learning. XPU provides high performance and energy efficiency for AI workloads, and PaddlePaddle’s integration with XPU allows users to seamlessly target this accelerator for their models.

The Key Advantages are:

• Scalability: Designed to handle ultra-large-scale models and distributed training across multiple nodes.
• Flexibility: Supports a wide range of hardware platforms and offers both static and dynamic graph execution modes.
• Performance: Leverages a sophisticated compiler infrastructure and optimized libraries for high performance.

XLA: Accelerated Linear Algebra for AI

Accelerated Linear Algebra (XLA) is a domain-specific compiler for linear algebra optimization originally designed for TensorFlow, but expanded to target a diverse set of frameworks (TensorFlow, JAX, PyTorch) and a diverse set of hardware. XLA utilizes MLIR for some components where appropriate, such as StableHLO, and utilizes LLVM for hardware device support, but utilizes a unique HLO IR and passes infrastructure instead of MLIR. XLA was developed and designed before MLIR was formed, and it influenced the development of MLIR. XLA is a pluggable compiler framework that can utilize IREE. It initially focused on linear algebra optimization.

XLA operates at the full-graph level and performs transformations accordingly. It also considers the topology of the target (for multi-devices environments) and performs all sharding/partitioning necessary, and optimizes the cross-device communication scheduling to overlap computations. It is parameterized and customized for a given execution environment (platform and runtime).

XLA restricts the IR to “functional” programs with no aliasing and mutations (side effects), unlike PyTorch Dynamo and Inductor. Many AI models, including ones with dynamic tensor shapes, may not run optimally in XLA. Special care needs to be taken to avoid graph breaks and graph recompilations.

IREE: MLIR-Based Framework for End-to-End AI Optimization

Intermediate Representation Execution Environment (IREE) is an MLIR-based framework to optimize machine learning models. It is an effort to develop an end-to-end AI compiler infrastructure completely in MLIR, ideally with all tensor operations generated from and by the compiler pipeline. IREE targets the entire range of ML hardware environments from data centers to mobile and edge deployments.

Model Support and Code Generation

IREE can utilize models expressed in PyTorch, TensorFlow, LiteRT (formerly TensorFlow Lite), JAX, and ONNX, and can generate code for CPUs, NVIDIA CUDA, AMD ROCm, and SPIR-V for a growing list of hardware accelerators. PyTorch connects to Torch-MLIR, and TensorFlow and JAX interface with the StableHLO IR into MHLO graph optimizer, progressively lowering to MLIR LinAlg dialect and eventually the IREE execution environment utilizing LLVM to generate CPU and GPU code.

MLIR Dialects and Optimization Frameworks

IREE utilizes the rich set of MLIR dialects, including Affine, Arith, LinAlg, MemRef, MHLO, StableHLO, SCF, Tensor, and TOSA to construct optimization passes. The dialects can be utilized in optimization passes to apply semantic-specific optimizations that permit complex and aggressive transformations relevant to dialect-specific representations of the objects and model.

IREE leverages these dialects to perform a wide range of optimizations, including:

• Operator Fusion: Combining multiple operations into a single kernel to reduce overhead and improve performance.
• Tiling: Decomposing computations into smaller tiles to improve data locality and cache utilization.
• Loop Unrolling: Duplicating loop bodies to reduce loop overhead and expose parallelism.
• Hardware-Specific Optimizations: Leveraging specific instructions and capabilities of the target hardware for maximum performance.

IREE’s focus on a unified MLIR-based infrastructure and its comprehensive approach to optimization make it a promising tool for accelerating AI workloads across diverse hardware platforms.

Edge AI Ecosystem

ExecuTorch: Streamlining AI Deployment on Mobile and Edge Devices

Mobile and edge devices have special requirements for AI deployment, including diverse hardware, critical power requirements, low or no internet connectivity, and realtime processing constraints. ExecuTorch is an emerging framework designed to facilitate the deployment of AI models on mobile and edge devices, complementing the capabilities of existing platforms like PyTorch. As part of the broader push towards enabling efficient on-device AI, ExecuTorch focuses on providing a streamlined runtime environment that optimizes PyTorch models for execution on constrained hardware. This includes support for various hardware accelerators and integration with platform-specific APIs to ensure optimal performance. ExecuTorch aims to leverage the flexibility and ease of use of PyTorch while introducing optimizations that reduce the computational footprint and power consumption of AI models. This makes it an attractive choice for developers looking to deploy sophisticated AI applications in scenarios where computational resources are limited, and real-time processing is crucial.

Model Transformation

ExecuTorch exports a PyTorch model graph in ATen dialect in which it can be transformed and optimized in architecture-agnostic ways. The ATen dialect is lowered to the Edge dialect that is aware of the parameterized constraints of the target device for additional specialization. The Edge dialect then is transformed to the Backend dialect that leverages delegates for specific hardware, allowing Core ML on iOS, QNN on Qualcomm, or TOSA on Arm to rewrite the graph. The resulting graph can be further prepared for the runtime environment through memory layout and usage planning, selectively linking actively-used kernels, and optimally serializing and packing the program for efficient loading and execution by the runtime. For optimal memory planning, tensor mutations need to be expunged, as required by XLA.

Target Backend and Dialects

ExecuTorch is designed to target a number of mobile hardware accelerators, including CPUs via XNNPACK, GPUs via Vulkan, Apple Neural Engine via Apple CoreML, and DSPs, with flexibility for additional targets.

Model Optimization

ExecuTorch applies a variety of optimizations to improve model performance on mobile and edge devices:

• Quantization: Reduces the precision of model weights and activations to lower memory usage and computational requirements.
• Pruning: Removes redundant connections or neurons in the model to reduce its size and complexity.
• Fusion: Combines multiple operations into a single kernel to reduce overhead and improve performance.
• Memory Planning: Optimizes memory layout and usage to reduce memory footprint and improve cache utilization. To ensure optimal memory planning, tensor mutations are eliminated as required by XLA (Accelerated Linear Algebra). This approach guarantees predictable and efficient memory usage, which is critical for mobile and edge deployments where resources are limited.
• Kernel Selection: Selectively links actively used kernels to minimize the runtime’s size.

By addressing the specific needs of mobile and edge AI deployment, ExecuTorch provides a robust framework that combines the strengths of PyTorch with targeted optimizations for constrained environments. This ensures that AI models can be efficiently deployed and executed on a wide range of devices, enabling real-time, on-device AI applications.

LiteRT: Enabling On-Device Machine Learning

Lite Runtime (LiteRT), formerly TensorFlow Lite (TFLite), is a lightweight, open-source deep learning framework designed specifically for mobile and edge devices. It enables developers to deploy machine learning models on resource-constrained environments such as smartphones, embedded systems, and IoT devices. LiteRT provides a suite of tools and APIs for optimizing and converting TensorFlow models into a format that is efficient for mobile and edge hardware. Its interpreter is optimized for low latency and high performance, supporting hardware acceleration on various platforms, including Android Neural Networks API (NNAPI), GPU, and Hexagon DSP. LiteRT’s ability to perform on-device inference ensures real-time data processing and reduced dependency on cloud services, making it ideal for applications requiring low latency, privacy, and offline capabilities.

Use cases

LiteRT has been widely adopted across various industries and applications:

• Mobile Apps: LiteRT powers on-device AI features in mobile apps, such as real-time image recognition, language translation, and voice assistants.
• Embedded Systems: LiteRT enables AI on devices like microcontrollers and IoT devices, allowing for intelligent edge computing in areas like smart homes, wearables, and industrial automation.
• Healthcare: LiteRT has been used to develop medical image analysis tools, disease prediction models, and personalized health monitoring applications.
• Finance: LiteRT can be leveraged for fraud detection, risk assessment, and personalized financial recommendations.
• Autonomous Systems: LiteRT is used in applications like object detection and classification for autonomous vehicles and drones.

Paddle Lite: Lightweight Inference for Edge Devices

Paddle Lite provides an ecosystem for mobile, embedded and edge devices. Paddle Lite supports acceleration and optimization strategies employing quantization, subgraph fusion, and kernel optimization to generate lightweight models tuned for edge devices. It natively supports a wide variety of hardware, including Android, iOS, Metal, QNN, Android NNAPI, many XPUs and NPUs.

Paddle Lite has been successfully deployed in various real-world applications, including:

• Mobile Applications: Image recognition, object detection, natural language processing tasks in apps.
• Embedded Systems: AI-powered cameras, smart home devices, industrial automation systems.
• IoT Devices: Real-time sensor data analysis, anomaly detection, predictive maintenance.
• Robotics: Object tracking, path planning, control systems for autonomous robots.

Modular: A Unified AI Infrastructure with the Mojo Language

Modular, both the company and the AI Infrastructure, is striving to reinvent the AI ecosystem utilizing their newly designed Mojo language built on the MLIR infrastructure. It is not strictly a framework or a compiler system, but rather a combination of both, along with other components to create a unified AI infrastructure.The language and optimization infrastructure provide a solution to reduce the fragmentation in the machine learning programming language environment to encourage innovation and simplify development and deployment of efficient machine learning models. Modular provides a Python-like language that targets performance of low-level languages like C Language. Modular leverages the MLIR architecture, but is developing a separate pipeline based on dialects distinct from the dialects used by IREE, MLIR-Turbine, ONNX-MLIR, and Triton-Shared instead of relying on the existing MLIR dialects used by these frameworks. Modular is developing its own set of dialects specifically tailored for the Mojo language and its optimization goals. This decision allows Modular to have full control over the compilation process and enables the development of language-specific optimizations and transformations.

Potential Use cases

• Research: Mojo could be used to develop and prototype high-performance AI models for research purposes.
• Production: Modular’s infrastructure could enable efficient deployment of Mojo-based models in production environments.
• Hardware Acceleration: Mojo’s ability to target different hardware architectures could be leveraged to accelerate specific AI workloads on specialized accelerators.

ONNX: A Bridge for Interoperability in the AI Ecosystem

ONNX is an open standard and open ecosystem that defines a common representation for machine learning algorithms and provides a set of software tools to convert among the frameworks and to the common ONNX Runtime. ONNX defines a standard for an interchangeable representation of the graph for a machine learning model, an ever-growing list of operators, and data types. Ideally, one can export the graph from any of the machine learning frameworks into a well-defined ONNX description with precise semantics and import the graph into another framework for further optimization and deployment. The wide variety of frameworks for interchange include PyTorch, TensorFlow, JAX, Apache MXNet, Tencent NCNN, and Baidu PaddlePaddle. The model can be transformed and optimized within the ONNX representation, and can be deployed through multiple ONNX-based frameworks, such as ONNX Runtime and ONNX-MLIR.

ONNX-MLIR and its Dialects

ONNX-MLIR provides a compiler framework based on MLIR that ingests ONNX models in an ONNX dialect and generates code for various targets, including x86_64, ARM, Power, and IBM Z. It can be extended to AI accelerator targets.

ONNX-MLIR leverages the ONNX, KRNL, LinAlg, and Affine MLIR dialects. The ONNX dialect represents native ONNX operators and operations. The KRNL dialect represents the lowering of ONNX operators to loops. The KRNL dialect provides facilities for tiling, fusion, parallelization by recording the optimization to build a recipe of loop optimizations that can be deployed, but allowing the optimizations to be reverted depending on impacts and interactions with other optimizations and transformations.

Benefits

Framework Interoperability: Enables seamless model exchange between frameworks like PyTorch, TensorFlow, JAX, and others.
Hardware Optimization: ONNX Runtime and ONNX-MLIR provide access to hardware-specific optimizations for improved performance on various devices.
Simplified Deployment: Allows developers to deploy models on different platforms without rewriting or retraining them.
Community and Ecosystem: ONNX has a large and active community with a growing ecosystem of tools and libraries.

Use cases

Microsoft Windows ML: Uses ONNX Runtime to deploy machine learning models on Windows devices.
Facebook Caffe2: Supports exporting models to ONNX for broader compatibility and deployment.
NVIDIA TensorRT: Integrates with ONNX Runtime for efficient deployment of deep learning models on NVIDIA GPUs.

NNEF: Neural Network Exchange Format

Neural Network Exchange Format (NNEF) is an open standard developed by the Khronos Group to facilitate the exchange of trained neural networks among different frameworks and inference engines. Similar to ONNX, NNEF aims to promote interoperability and portability in the machine learning ecosystem, enabling the deployment of neural networks across diverse platforms and devices.

The NNEF standard defines a common representation format for neural networks, which captures the structure and parameters of the model. This format is designed to be simple, flexible, and expressive, allowing the representation of a wide range of neural network architectures and operations. By providing a standardized way to describe neural networks, NNEF enables the seamless transfer of models between different frameworks and inference engines.

One of the key advantages of NNEF is its focus on deployment and inference. While other exchange formats, such as ONNX, cover a broader range of machine learning models and tasks, NNEF specifically targets neural networks and is optimized for efficient inference on various platforms. This specialization allows NNEF to provide a streamlined and optimized representation that can be easily mapped to target hardware.

NNEF supports a comprehensive set of operations and data types commonly used in neural networks, including convolution, pooling, activation functions, and tensor manipulation. The standard also defines a set of best practices and guidelines for representing neural networks, ensuring consistency and compatibility across different implementations.

To facilitate the adoption and use of NNEF, the Khronos Group provides a software development kit (SDK) that includes tools and libraries for working with NNEF models. The SDK enables developers to convert neural networks from popular frameworks, such as TensorFlow and PyTorch, into the NNEF format. It also provides APIs and utilities for manipulating NNEF models, applying optimizations, and integrating them into inference engines or target platforms.

One of the strengths of NNEF is its focus on efficiency and performance. The standard is designed to minimize the overhead of model translation and enable direct mapping to target hardware. This allows inference engines and deployment platforms to leverage the full potential of the hardware, resulting in faster and more efficient execution of neural networks.

Benefits

Broad Compatibility: NNEF supports a wide range of machine learning models, including both deep learning and classical machine learning algorithms.

Hardware Agnosticism: NNEF models can be deployed on various hardware platforms, including CPUs, GPUs, and specialized AI accelerators.

Extensibility: NNEF is designed to be extensible, allowing for the addition of new operators and features as the field of AI evolves.

Simplified Workflow: NNEF simplifies the deployment of AI models by providing a standardized format and tools for conversion and optimization.

Use cases:

Khronos Group: NNEF is developed and maintained by the Khronos Group, a consortium of industry leaders working on open standards for graphics and compute.
NNStreamer: A GStreamer-based multimedia framework that supports NNEF for efficient processing of AI workloads on various hardware platforms.
Embedded and Mobile Devices: NNEF is well-suited for deployment on resource-constrained devices, enabling on-device AI applications.

PolyBlocks: MLIR-Based Compiler for High-Dimensional Data Spaces

PolyMage Labs has created PolyBlocks based on MLIR infrastructure. PolyBlocks is based on the MLIR infrastructure. It can support a variety of programming languages and frameworks (like PyTorch, TensorFlow, and JAX) as well as hardware targets. PolyBlocks particularly employs polyhedral optimization techniques and specialize in compiling computations on high-dimensional data spaces. PolyBlocks optimization techniques encompass tiling, fusion, performing recomputation (in conjunction with tiling and fusion), packing into on-chip buffers for locality, eliminating intermediate tensors or shrinking intermediate tensors to bounded buffers fitting into on-chip memory, mapping to matmul/tensor cores, and efficient parallelization in a way unified with all other transformations.

This offers Technical Advantages of Efficient Handling of High-Dimensional Data, where PolyBlocks excels at optimizing computations involving high-dimensional tensors, common in deep learning models. It can effectively exploit parallelism and data locality to achieve high performance. It also offers a Unified Optimization Framework unlike some compilers that rely on separate optimization passes for different transformations, PolyBlocks integrates all optimizations within a unified framework, leading to more effective overall optimization. It is Hardware Agnosticism as PolyBlocks can target a variety of hardware architectures, including CPUs, GPUs, and specialized AI accelerators, by leveraging MLIR’s hardware abstraction capabilities.

Use cases

Deep Learning: PolyBlocks has been used to accelerate training and inference of deep learning models in various domains, such as natural language processing, computer vision, and reinforcement learning.
Scientific Computing: It has also found applications in scientific computing, optimizing complex simulations and numerical calculations involving high-dimensional data.

TVM: An End-to-End Machine Learning Compiler Stack

TVM is an ecosystem for compiling, optimizing, and tuning ML models produced by various frameworks, including PyTorch, TensorFlow, ONNX, Keras. The TVM compiler stack is based on IRModule to apply a series of optimizations tailored for different hardware targets. These optimizations include operator fusion, memory optimization, and hardware-specific code generation, with its own, extensible compiler infrastructure to transform the IR in various optimization stages. TVM targets a variety of target architectures and runtimes. It has Target-Agnostic Scheduling where TVM employs a target-agnostic scheduling mechanism that separates the description of the computation from the underlying hardware details. This allows developers to optimize their models once and deploy them on different platforms without significant modifications.
Another component called Automatic Tuning (AutoTVM) of the TVM ecosystem is infrastructure to automatically optimize and tune models based on profiling feedback. There is also Tensor Expression Language (TE) which allows developers to express high-level computations concisely. This facilitates the exploration of different optimization strategies and enables the generation of optimized code for various hardware backends.

TVM also includes the experimental Versatile Tensor Accelerator (VTA) optimizing compiler framework for machine learning. VTA is built on its own compiler infrastructure, which includes its own intermediate representation, a graph optimizer, and a tensor optimizer.

Use Cases

Model Deployment: TVM has been widely used to deploy deep learning models on various hardware platforms, achieving significant performance improvements and cost savings compared to traditional approaches.

Embedded Systems: TVM’s ability to optimize models for resource-constrained devices makes it ideal for deploying AI on edge devices, such as smartphones, IoT devices, and embedded systems.

Hardware Acceleration Research: VTA provides a platform for research and development of new AI accelerator architectures, enabling rapid prototyping and experimentation.

Ecosystem Integration

TVM integrates with various tools and frameworks:

• Relay: A functional graph intermediate representation for representing and optimizing deep learning models.
• AutoScheduler: A newer automatic scheduling system that complements AutoTVM, providing even more efficient model optimization.
• Micro TVM: A framework for deploying TVM models on bare-metal microcontrollers.
• BYOC (Bring Your Own Codegen): A mechanism for integrating custom code generators and operators into the TVM stack.

The Generalized Acceleration Languages

CUDA: Empowering GPU Acceleration for AI

Compute Unified Device Architecture (CUDA) is a programming model and environment in which to implement numerically intensive computations for NVIDIA GPUs. CUDA targets the GPU virtual instruction set, allowing fine-grained control of computation and data placement within the memory hierarchy.

CUDA’s success in the AI/ML ecosystem can be attributed to several key factors, particularly its implementation as a single-source C++ embedded domain-specific language (DSL), its use of the Single Instruction, Multiple Threads (SIMT) model, and its strong commitment to backward compatibility throughout its evolution. The single-source C++ DSL approach allows developers to write both host and device code within the same file, simplifying the development process and enabling seamless integration with existing C++ applications. The SIMT model, which allows thousands of threads to execute the same instruction simultaneously on different data, effectively leverages the massive parallel processing power of GPUs, making it highly efficient for a wide range of computational tasks.

Parallel Thread Execution (PTX) is a low-level, parallel thread execution virtual machine and instruction set architecture for the NVIDIA CUDA programming environment. PTX provides a stable instruction set and environment for CUDA and the NVCC compiler. The PTX instruction set is translated to the instruction set of the hardware accelerator by the device driver. CUDA’s backward compatibility ensures that code written for earlier devices continues to run on newer hardware and software platforms without modification, providing developers with confidence that their investments in CUDA development (knowledge, skills, experience, tools, codebase, and applications) will remain valuable over time.

Library and ecosystem

One notable example is the cuDNN library, which provides highly optimized primitives for deep learning operations, such as convolutions, pooling, and activation functions. cuDNN has become an essential component in many deep learning frameworks, enabling efficient training and inference on NVIDIA GPUs.

Another important development in CUDA is the introduction of tensor cores, specialized hardware units designed for accelerating matrix multiplication and convolution operations. Tensor cores provide significant performance gains for deep learning workloads, and CUDA has been extended to support programming these cores efficiently.

CUDA’s extensive ecosystem, which includes a wide range of libraries, tools, and frameworks, has also contributed to its popularity. From domain-specific libraries like cuBLAS (linear algebra) and cuFFT (fast Fourier transforms) to higher-level frameworks like cuDNN and TensorRT, CUDA provides a rich set of resources that enable developers to build high-performance applications with ease.

While CUDA is the dominant GPU programming model, it is specific to NVIDIA GPUs, limiting its portability to other hardware platforms. Alternatives like OpenCL and SYCL offer more open and vendor-neutral approaches.

This combination of ease of use, powerful parallel processing capabilities, and a stable, evolving platform has made CUDA the preferred choice for many in the AI/ML community.

ROCm: Open-Source Software Stack for AMD GPUs

Radeon Open Compute (ROCm) is a software stack that can be used to implement numerically intensive computations for AMD GPUs. It offers a comprehensive suite of tools, libraries, and frameworks, including compilers, debuggers, and performance analysis tools, aimed at facilitating the development and optimization of applications on AMD hardware.

At the core of ROCm is the Heterogeneous-computing Interface for Portability (HIP) programming environment, an embedded C++ DSL for writing kernels for both NVIDIA and AMD GPUs, similar to CUDA for Nvidia and SYCL for all forms of accelerators ROCm also provides OpenCL compilers, as well as support for GPU-accelerated compilation through LLVM. ROCm offers GPU debugging tools and performance analysis tools to help developers identify and resolve issues in their GPU code. ROCm includes optimized libraries for common mathematical operations (e.g., rocBLAS for linear algebra) and GPU-accelerated communication (e.g., rccl for multi-GPU and multi-node communication). ROCm supports popular frameworks like TensorFlow and PyTorch, allowing developers to leverage AMD GPUs for AI and machine learning workloads.

There is also a HIPify translation facility to adapt CUDA code to AMD GPUs. This tool can automatically translate many CUDA constructs to their HIP equivalents, significantly reducing the effort required to port existing CUDA applications to run on AMD GPUs. While not all CUDA features have direct equivalents in HIP, the tool can handle a large portion of common GPU programming patterns, making it easier for developers to target multiple GPU platforms.

Open Source

One of the key strengths of ROCm is its open-source nature. Unlike proprietary solutions, ROCm provides full access to its source code, encouraging community contributions and fostering transparency in development. This open approach allows developers to customize and extend the platform to meet their specific needs, potentially leading to more rapid innovation and problem-solving.

Together, ROCm and HIP empower developers to harness the full potential of AMD GPUs, providing a flexible and efficient platform for a wide range of computationally intensive applications.

SYCL: Open Acceleration with C++ for any accelerator

SYCL is a Khronos embedded domain-specific language that provides a Standard C++ abstraction layer, similar to CUDA and HIP, to enable heterogeneous programming and to improve productivity and efficiency for diverse hardware accelerators. The latest SYCL is built on standard C++17 and later versions, allowing developers to leverage modern C++ features and existing codebases, with the intention to follow the ISO C++ standard release. SYCL uses C++ templates extensively, enabling compile-time optimizations and type safety.

The first implementations of SYCL used OpenCL but now implementations use various device-specific back-ends as well as OpenCL.

SYCL allows developers to write code that runs on a variety of hardware accelerators, such as GPUs, CPUs, FPGAs, and more, within a single source code.

oneAPI DPC++ is an implementation of SYCL and is a downstream fork of the LLVM Clang project that is in the process of being upstreamed to the LLVM Clang project. DPC++ implements multiple backends that are adapted for different GPU target languages including SPIR-V, PTX and GCN. SYCL can utilize the Standard Portable Intermediate Representation (SPIR-V), implemented as a dialect in MLIR.

SYCL provides constructs for expressing both data parallelism (same operation on multiple data elements) and task parallelism (different operations running concurrently). This allows developers to leverage the full parallelism of modern hardware. SYCL’s buffer/accessor model is an implicit data movement model that simplifies data management between the host and devices, abstracting away the complexities of memory transfers and synchronization. It also offers an explicit data movement model.

SYCL code can interoperate with other programming models and libraries, such as OpenMP, MPI, and CUDA, allowing developers to leverage existing code and libraries. Recent development of a SYCL SC WG adds Safety Critical to enable SYCL in automotive, flight controls, space, and any safety-critical applications that might need to follow ISO-26262, 61508, and/or 21448.

SYCL in the AI Ecosystem

SYCL is well positioned to provide a foundation on which to enhance numerically intensive algorithms written in C++, with or without CUDA and HIP, to exploit a broad range of diverse hardware accelerators. It simplifies the creation of optimized kernels that can be deployed in libraries, such as SYCL-DNN and SYCL-BLAS, for use by machine learning frameworks. SYCL can be utilized in machine learning frameworks such as PyTorch and TensorFlow, to write operators similar to CUDA and HIP, targeting a wide variety of hardware accelerators. SYCL complements and integrates with the compiler infrastructure for AI models by delivering the numerically intensive kernel libraries and operations called by the AI model frameworks.

SYCL enables the acceleration of various high-performance computing applications beyond AI, such as scientific simulations and financial modeling. As SYCL follows C++ and uses C++ templates, it can use compile-time programing constructs such as macros, inlining, expression templates, and metatemplate programming to support rapid rewrite of ML operators using different template types passed in as arguments.

Advantages of SYCL

1. Portability: SYCL code can run on a wide range of hardware without modification, reducing vendor lock-in.
2. Performance: It allows developers to write high-performance code that can leverage hardware-specific optimizations.
3. Productivity: The single-source programming model simplifies development and maintenance of heterogeneous applications.
4. Standards-Based: Being an open standard ensures long-term support and community-driven improvements.

SYCL’s ability to target multiple hardware platforms makes it an attractive option for developers seeking to create portable, high-performance AI and ML applications.

SYCL is a powerful tool for accelerating a wide range of applications on diverse hardware. Its single-source C++ approach, multi-backend support, and advanced parallelism features make it an attractive option for developers seeking performance and portability.

OpenCL: Open Acceleration with C for compute and AI/ML

OpenCL (Open Computing Language) is an open standard for parallel programming of heterogeneous systems, managed by the Khronos Group. While it’s a general-purpose framework, OpenCL has found significant application in the AI and machine learning domains due to its ability to leverage diverse hardware accelerators.

The Key Features of OpenCL for AI/ML are Heterogeneous Computing where OpenCL allows developers to write code that can run on various hardware accelerators, including CPUs, GPUs, FPGAs, and specialized AI processors. It is also about Portable Performance where it provides a low-level API that enables fine-grained control over hardware resources, allowing for highly optimized implementations of AI and ML algorithms. It is Vendor-Neutral. As an open standard, OpenCL supports hardware from multiple vendors, reducing dependency on proprietary solutions. Finally, it enables Kernel Programming where OpenCL uses a C-based kernel language, allowing developers to write custom kernels for specialized AI operations.

OpenCL in the AI/ML Ecosystem:

1. Deep Learning Frameworks: Some deep learning frameworks, such as Caffe and Keras, have OpenCL backends, enabling the execution of neural networks on OpenCL-compatible devices.
2. Tensor Libraries: Libraries like CLBlast provide OpenCL implementations of BLAS (Basic Linear Algebra Subprograms), which are fundamental to many ML algorithms.
3. Computer Vision: OpenCV, a popular computer vision library, supports OpenCL acceleration for various image processing and machine learning tasks.
4. Scientific Computing: Libraries like ArrayFire, which are used in scientific computing and some ML applications, leverage OpenCL for hardware acceleration.

Advantages for AI/ML:

1. Hardware Flexibility: OpenCL allows AI models to run on a wide range of hardware, from high-performance GPUs to low-power embedded devices.
2. Custom Optimizations: Developers can write custom OpenCL kernels to optimize specific AI operations for particular hardware architectures.
3. Edge AI: OpenCL’s support for embedded and mobile processors makes it suitable for deploying AI models at the edge.
4. Legacy Hardware Support: It can leverage older hardware that may not be supported by newer, AI-specific frameworks.

Challenges and Considerations:

1. Complexity: OpenCL’s low-level nature can make it more complex to use compared to higher-level AI frameworks.
2. Performance Tuning: Achieving optimal performance often requires hardware-specific optimizations, which can be time-consuming.
3. Ecosystem Maturity: While growing, the ecosystem of OpenCL-based AI tools and libraries is less mature compared to CUDA or other AI-specific platforms.
4. Competition from AI-Specific Solutions: Frameworks like CUDA and ROCm, which are more tailored for AI workloads, often provide better out-of-the-box performance for common AI tasks.

Recent Developments:

1. OpenCL 3.0: The latest version of the standard focuses on a modular architecture, allowing implementations to support a core feature set with optional extensions. This approach aims to improve adoption across diverse hardware platforms.
2. Integration with SYCL: There’s ongoing work to enable interoperability between OpenCL and SYCL, potentially combining OpenCL’s widespread support with SYCL’s more modern C++ programming model.
3. AI-Specific Extensions: Some vendors are developing OpenCL extensions specifically for AI and ML workloads, enhancing its capabilities in this domain.

Use Cases in AI/ML:

1. Custom AI Accelerators: Companies developing specialized AI hardware often provide OpenCL support as a way to enable software ecosystem development.
2. Research and Prototyping: OpenCL’s flexibility makes it useful for researchers exploring novel AI algorithms or hardware architectures.
3. Cross-Platform AI Deployment: Organizations needing to deploy AI models across a diverse hardware landscape may leverage OpenCL for its portability.
4. Edge AI and IoT: OpenCL’s support for embedded processors makes it valuable for deploying AI models in edge and IoT devices.

While OpenCL faces strong competition from AI-specific frameworks and APIs, its open nature, hardware flexibility, and low-level control continue to make it a relevant tool in the AI and ML ecosystem, particularly for specialized or cross-platform applications.

OpenVX: Accelerating Computer Vision for AI/ML Applications

OpenVX (Open Visual Acceleration) is a Khronos open standard API for computer vision and machine learning acceleration, developed and maintained by the Khronos Group. While initially focused on computer vision, OpenVX has evolved to include significant support for neural network inference, making it increasingly relevant in the AI and machine learning domains, particularly for edge computing and embedded systems.

The Key Features of OpenVX for AI/ML are Graph-based Execution where OpenVX uses a graph-based execution model, which is well-suited for representing and optimizing neural network architectures.It also provides a hardware-agnostic API, allowing developers to write portable code that can run efficiently on various accelerators (CPUs, GPUs, DSPs, and dedicated vision/AI processors). OpenVX offers a set of pre-defined, performance-optimized functions (kernels) for common computer vision and machine learning operations.The OpenVX Neural Network Extension provides specific support for deep learning inference, including popular layer types and operations.

OpenVX in the AI/ML Ecosystem:

1. Edge AI Deployment: OpenVX is particularly well-suited for deploying AI models on edge devices and embedded systems, where power efficiency and real-time performance are critical.
2. Computer Vision Integration: It seamlessly combines traditional computer vision operations with neural network inference, enabling efficient implementation of complex vision-AI pipelines.
3. Inference Optimization: OpenVX can be used to optimize the inference stage of neural networks, particularly for vision-related tasks.
4. Hardware Acceleration: Many hardware vendors provide OpenVX implementations optimized for their specific accelerators, enabling efficient execution of AI workloads.

Advantages for AI/ML:

1. Performance and Efficiency: The graph-based model allows for global optimizations, leading to improved performance and energy efficiency.
2. Portability: OpenVX code can run on various hardware platforms without modification, simplifying deployment across different devices.
3. Safety and Security: OpenVX includes provisions for safety-critical systems, making it suitable for AI applications in automotive, robotics, and other sensitive domains.
4. Integration with Vision Systems: It provides a standardized way to integrate AI capabilities into existing vision processing pipelines.

Challenges and Considerations:

1. Limited to Inference: OpenVX primarily focuses on inference, not training, which limits its applicability in the full AI development cycle.
2. Ecosystem Maturity: While growing, the ecosystem of OpenVX-based AI tools and model converters is less mature compared to some other frameworks.
3. Learning Curve: The graph-based programming model may require a different approach compared to traditional imperative programming.
4. Scope Limitation: While powerful for vision-related AI tasks, OpenVX may be less suitable for non-vision AI applications.

Recent Developments:

1. Enhanced NN Support: Recent versions have significantly improved support for neural network operations, including more layer types and optimizations.
2. Tensor Support: Improved handling of tensors, essential for modern deep learning models.
3. Safety-Critical Systems: Ongoing work to enhance OpenVX’s suitability for safety-critical AI applications, particularly in automotive and industrial settings.

Use Cases in AI/ML:

1. Autonomous Vehicles: OpenVX is used in implementing vision and AI systems for advanced driver-assistance systems (ADAS) and autonomous driving.
2. Smart Cameras: AI-enhanced surveillance and monitoring systems leverage OpenVX for efficient on-device processing.
3. Industrial Automation: Vision-based quality control and robotic guidance systems utilize OpenVX for real-time AI inference.
4. Augmented Reality: AR applications use OpenVX to efficiently combine computer vision and AI for real-time scene understanding and overlay generation.

Vulkan: Unleashing GPU Potential for AI/ML Applications

Vulkan from Khronos, originally designed as a low-overhead, cross-platform 3D graphics and compute API for games, has been increasingly adopted for general-purpose computing, including applications in artificial intelligence and machine learning. While not primarily designed for AI/ML workloads, Vulkan’s efficiency and cross-platform nature make it an attractive option for certain AI/ML tasks, particularly in mobile and embedded systems. Vulkan is platform-agnostic, meaning it can run on various operating systems, including Windows, Linux, and Android. This cross-platform capability ensures that AI/ML applications can be developed and deployed across a wide range of devices.

The Key Features of Vulkan for AI/ML are Cross-Platform Support where Vulkan runs on a wide range of devices, from high-performance GPUs to mobile processors, enabling consistent AI deployment across diverse hardware. It has Low-Overhead Design. Its efficient design allows for better utilization of hardware resources, which is crucial for performance-intensive AI workloads. Vulkan’s compute capabilities, through compute shaders, can be leveraged for parallel processing in AI/ML algorithms. These are crucial for performing general-purpose computations on the GPU. Compute shaders can be used to accelerate neural network training and inference, matrix operations, and other AI/ML computations.There is Fine-Grained Control where: Vulkan provides low-level control over hardware, allowing for optimizations specific to AI/ML tasks. Explicit memory management in Vulkan can be advantageous for optimizing memory-intensive AI models. This allows developers to optimize data movement and processing, reducing latency and improving throughput for AI/ML workloads.

Vulkan in the AI/ML Ecosystem:

1. Mobile AI: Vulkan is particularly well-suited for deploying AI models on mobile devices, where power efficiency and cross-device compatibility are crucial.
2. Edge Computing: Its efficiency makes Vulkan a good choice for AI inference at the edge, where resources may be limited.
3. Computer Vision: Vulkan’s roots in graphics make it naturally suited for computer vision tasks that combine rendering and AI processing.
4. General-Purpose GPU Computing: Some developers use Vulkan as an alternative to CUDA or OpenCL for general-purpose GPU computing in AI applications.

Advantages for AI/ML:

1. Performance: Vulkan’s low-overhead design can lead to performance improvements in AI inference tasks, especially on mobile and embedded devices.
2. Portability: AI models using Vulkan can potentially run on a wide range of devices without modification.
3. Integration with Graphics: For AI applications that involve both rendering and inference (e.g., AR/VR), Vulkan provides a unified API for both tasks.
4. Hardware-Specific Optimizations: Vulkan allows developers to implement hardware-specific optimizations for AI tasks while maintaining a common API.
5. Custom Kernel Development: Developers can write custom compute shaders tailored to specific AI/ML tasks, optimizing performance for unique use cases.
6. Integration with Other APIs: Vulkan can be integrated with other Khronos APIs like OpenCL and SYCL, allowing developers to build versatile and high-performance AI/ML applications.

Challenges and Considerations:

1. Complexity: Vulkan’s low-level nature can make it more complex to use compared to higher-level AI-specific frameworks.
2. Limited AI-Specific Features: Unlike APIs designed specifically for AI, Vulkan lacks built-in operations for common AI tasks, requiring more from-scratch implementation.
3. Ecosystem Maturity: The ecosystem of AI tools and libraries for Vulkan is less developed compared to established AI platforms like CUDA or TensorFlow.
4. Learning Curve: Developers familiar with high-level AI frameworks may find Vulkan’s low-level approach challenging.

Recent Developments:

1. Vulkan 1.2 and Beyond: Recent versions of Vulkan have introduced features that can benefit AI workloads, such as improved synchronization and more flexible shader capabilities.
2. Machine Learning Extensions: Some vendors are working on Vulkan extensions specifically tailored for machine learning operations.
3. Integration with AI Frameworks: Efforts are underway to better integrate Vulkan with popular AI frameworks, making it easier to use Vulkan as a backend for AI inference.

Use Cases in AI/ML:

1. Mobile AI Applications: Vulkan is used for deploying efficient AI models in mobile apps, particularly for tasks like image recognition or real-time video processing.
2. Augmented Reality: AR applications leverage Vulkan for both rendering and AI-based scene understanding.
3. Game AI: Some game developers use Vulkan for both graphics and AI computations in game engines.
4. Scientific Computing: Researchers in fields like computational physics or bioinformatics sometimes use Vulkan for GPU-accelerated AI and data processing tasks.

Challenges of Compatibility

The rapid evolution of AI hardware accelerators, such as GPUs, TPUs, FPGAs, and ASICs, has created a dynamic and diverse ecosystem.

Diverse Programming Models

Each type of hardware comes with its own set of architectural designs, performance characteristics, and operational constraints. This diversity, while beneficial in providing options for specific AI workloads, poses significant challenges for compatibility with AI software frameworks. Ensuring that frameworks like PyTorch, TensorFlow, JAX, Pallas, and Triton can fully leverage the capabilities of these accelerators requires substantial effort in developing and maintaining compatibility layers. These layers must translate high-level machine learning abstractions into low-level hardware instructions, which often involves dealing with intricate details of memory management, parallelization, and hardware-specific optimizations.

Continuous Updates and Optimizations

One of the primary challenges in achieving seamless compatibility is the need for continuous updates and optimizations. AI hardware manufacturers frequently release new versions of their accelerators with enhanced features and performance improvements. Consequently, AI frameworks must constantly evolve to support these new capabilities. This involves not only updating the core framework but also ensuring that the vast ecosystem of third-party libraries and tools remains compatible. Additionally, the differences in programming models across various accelerators add another layer of complexity. For instance, while GPUs are primarily programmed using CUDA and Triton, other accelerators like TPUs and FPGAs may use entirely different programming languages and interfaces, necessitating extensive cross-compatibility efforts.

Framework-Specific Optimizations

Achieving optimal performance across different hardware accelerators often requires framework-specific optimizations. These optimizations are not one-size-fits-all; they must be tailored to the unique characteristics of each accelerator. This process involves deep technical knowledge of both the hardware and the software stack. Developers must balance the need for generality in framework design with the need for hardware-specific performance tuning. For example, TensorFlow’s XLA (Accelerated Linear Algebra) compiler is designed to optimize computations for various hardware backends, but it requires meticulous configuration and testing to ensure it performs well on each supported accelerator. Similarly, PyTorch’s integration with custom backends like ROCm for AMD GPUs involves ongoing collaboration between hardware vendors and the open-source community to maintain and improve compatibility and performance.

Performance Optimization

Performance optimization in the context of AI hardware accelerators involves a multifaceted approach to ensure that AI models run efficiently and quickly across diverse platforms.

Hardware-Specific Libraries and Compilers

One of the primary strategies is to leverage hardware-specific libraries and compilers that are tailored to the unique capabilities of each accelerator. For instance, NVIDIA’s CUDA library is designed to exploit the parallel processing power of GPUs, enabling substantial speed-ups in training and inference tasks. Similarly, Google’s Tensor Processing Units (TPUs) benefit from TensorFlow’s XLA (Accelerated Linear Algebra) compiler, which optimizes computations specifically for TPUs. By utilizing these specialized tools, developers can significantly enhance the performance of their AI models on targeted hardware.

Mixed Precision Training

Another critical aspect of performance optimization is the implementation of mixed precision training. This technique involves using lower-precision arithmetic (such as 16-bit floating-point) for certain operations, which can dramatically reduce memory usage and increase computational speed without significantly impacting model accuracy. Frameworks like PyTorch and TensorFlow provide built-in support for mixed precision training, allowing developers to seamlessly integrate this optimization into their workflows. By taking advantage of hardware capabilities that support lower precision arithmetic, such as NVIDIA’s Tensor Cores, developers can achieve considerable performance gains. Additionally, mixed precision training often leads to faster data transfers and reduced latency, further enhancing overall efficiency.

Optimizing Data Pipelines

Furthermore, optimizing data pipelines and minimizing data movement are essential strategies for maximizing performance across different AI hardware platforms. Efficient data handling ensures that the accelerators are fed with data at a rate that matches their processing speed, preventing bottlenecks. Techniques such as data prefetching, sharding, and parallel I/O operations are commonly employed to optimize data pipelines.

Memory Hierarchy Optimization

Additionally, placing data closer to the computation units, through techniques like memory hierarchy optimization and using high-bandwidth memory (HBM), can reduce latency and improve throughput. Frameworks like JAX and Triton enable fine-grained control over data placement and memory management, allowing developers to tailor their data handling strategies to the specific requirements of their hardware accelerators. By focusing on these data optimization strategies, developers can ensure that their AI models perform efficiently and effectively across a range of hardware platforms.

The Role of Frameworks

AI frameworks like PyTorch and TensorFlow play a crucial role in abstracting away hardware complexities and providing developers with high-level APIs. However, they also face the challenge of maintaining compatibility and performance across the ever-expanding hardware landscape. This requires ongoing collaboration between framework developers, hardware vendors, and the open-source community to ensure that AI models can seamlessly run on various accelerators without sacrificing performance.

By continuously adapting to new hardware, incorporating optimization techniques, and collaborating with the wider community, AI frameworks can continue to evolve and empower developers to harness the full potential of AI hardware accelerators.

Navigating the Ecosystem

When selecting the appropriate AI framework and hardware accelerator for a machine learning project, developers and researchers should consider several key factors to ensure optimal performance and efficiency.

Project Requirements

First, the specific requirements of the project should guide the choice: different frameworks and hardware are better suited to certain types of tasks (e.g., image processing, natural language understanding, or real-time inference). The size and complexity of the model, as well as the need for real-time processing, dictate whether more special-purpose, compute-intensive accelerators like GPUs, TPUs, or NPUs should be utilized over CPUs.

Hardware Compatibility

Additionally, compatibility between the chosen framework and hardware is crucial; for example, TensorFlow integrates seamlessly with Google TPUs, while PyTorch has strong support for CUDA-enabled NVIDIA GPUs, and increasing support for AMD GPUs.

Scalability

Developers should also evaluate the scalability of the framework and hardware, considering whether the project will scale up to require distributed computing resources.

Ecosystem and Community

The ecosystem and community support surrounding a framework can provide additional resources and tools, enhancing development efficiency.

Budget and Availability

Lastly, budget constraints and availability of hardware resources must also be factored in, balancing cost against computational needs and the potential for future expansion. By carefully assessing these criteria, developers and researchers can effectively match their project requirements with the most suitable AI framework and hardware accelerator.

Leading Hardware Accelerator Vendors and Their Strategies

NVIDIA GPU has the advantage as an early mover and widespread adoption. NVIDIA has created a rich ecosystem of optimized libraries and tools, and is continually expanding into new markets to create more demand for its GPU hardware. It likely has a long pipeline of features that it is releasing and enabling as necessary to maintain momentum and sales. Most AI tools target NVIDIA GPUs first because of its pervasiveness and flexibility. Also, NVIDIA produces compatible consumer and data center GPUs, which allows developers to experiment with AI models and NVIDIA tools on their home systems. The voracious appetite for machine learning computation has generated supply constraints and high prices.

AMD GPU has chosen to address the AI accelerator market with its own GPU. AMD continues to expand its software ecosystem. As a GPU-type solution, AMD has significant alignment with NVIDIA. AMD is able to effectively translate and run most CUDA code through its HIPify tool. It also is relatively easy for tools targeting NVIDIA CUDA to add support for AMD ROCm with similar semantics. AMD has created novel designs for its GPU processors that provide benefits in cost-performance of some workloads.

Intel markets the Gaudi processor, originally developed by Habana, as its AI accelerator solution. The Gaudi processor differs from the GPU design, with advantages and disadvantages. It is based on a heterogeneous architecture of fully programmable Tensor Processing Cores (TPCs). Models in popular frameworks can be migrated to operate on Gaudi processors.

Google has developed its own architecture for AI, called Tensor Processing Units (TPUs), based on multiply-add systolic arrays. The TPU architecture is very powerful and efficient for some operations, but it exchanges that benefit for some limitations in flexibility. Novel AI model networks don’t always efficiently map to the TPU network. Google has created very powerful tools in XLA that can be exploited by its TensorFlow, JAX, and Pallas environments to develop models, optimize for its hardware, and deploy to its cloud.

Emerging Technologies and tools: Shaping the Future

Triton, IREE (Intermediate Representation Execution Environment), and MLIR (Multi-Level Intermediate Representation) are cutting-edge tools and libraries designed to enhance cross-platform compatibility and optimization for diverse AI hardware accelerators. These technologies are crucial in addressing the complexities involved in deploying machine learning models across various types of hardware without sacrificing performance.

Emerging technologies are poised to significantly reshape the compatibility and performance landscape for machine learning models deployed on diverse AI accelerator hardware.

Next-Generation Accelerators

As advancements in semiconductor technology lead to more powerful and energy-efficient accelerators, such as next-generation GPUs, TPUs, and specialized ASICs, the potential for AI applications expands dramatically.

Quantum and Neuromorphic Computing

Concurrently, developments in quantum computing and neuromorphic computing offer new paradigms for processing AI workloads, potentially accelerating tasks that are computationally intensive on traditional architectures.

Software Advancements

Software innovations, including more adaptive and intuitive AI frameworks, are evolving to harness these hardware advancements seamlessly. These frameworks are likely to incorporate more sophisticated optimization techniques, auto-tuning capabilities, and cross-platform compatibility features, enabling models to dynamically adapt to the most effective computing resources available.

Interoperability Standards

Additionally, emerging standards and protocols for AI model interoperability and data handling (such as ONNX for model exchange) will play crucial roles in enhancing the synergy between diverse hardware accelerators and software tools, ensuring that AI deployments can maximize efficiency regardless of the underlying technology stack.

Looking Ahead: A More Unified AI Ecosystem

This integration of emerging technologies will not only improve the performance and efficiency of AI models but also broaden their applicability across different sectors and complex problem domains. The future of AI hardware and software compatibility lies in a more unified ecosystem. As the technology matures, we can expect to see increased standardization, improved interoperability, and more streamlined workflows for deploying AI models across diverse hardware.

Best Practices for AI Performance developers and engineers

The machine learning (ML) ecosystem offers a rich toolbox for balancing efficiency, portability, and performance. However, choosing the right tools requires understanding project goals and deployment constraints.

Hybrid Optimization

State-of-the-art training and inference systems leverage a combination of techniques for optimal performance and cost-effectiveness. Critical path kernels often benefit from hand-written and tuned CUDA code for maximum speed. The “long-tail” of less critical kernels can be written in high-performance frameworks like Triton and Pallas. These frameworks provide abstractions and automation that simplify the development process without sacrificing too much performance.

Optimized Tensor Kernels

Optimized tensor kernels, GEMM, and convolutions also can be achieved through SYCL, CUDA, and HIP with C++ metaprogramming techniques, like those used in Eigen. These approaches allow developers to write kernels that can be easily adapted to different hardware architectures.

Optimized Libraries

For broader optimization, vendor-specific libraries like cuBLAS, cuDNN, CUTLASS (NVIDIA), ZenDNN (AMD), oneDNN (UXL), or SYCL-based libraries (SYCL-BLAS and SYCL-DNN) provide optimized algorithms for common ML operations. These libraries are tuned for specific hardware and offer significant performance gains out of the box. Leveraging these libraries can save development time and effort while ensuring optimal performance on the target hardware.

Framework-Level Optimization

Additionally, popular frameworks like PyTorch, TensorFlow, JAX, and ONNX all employ optimizing model graph compilers to generate efficient code and integrate optimized kernels. These compilers analyze the computation graph, apply optimizations such as operator fusion and memory layout transformations, and generate optimized code for the target hardware. This transforms models into efficient representations for execution on specific hardware.

Runtime Optimization

Runtimes like NVIDIA TensorRT further enhance the efficiency of compiled models by applying additional optimizations at runtime to complement the entire compiler and kernel stack. Optimal training and inference requires efficiently scheduling the ballet of control logic, optimized kernels, and memory moves to ensure that the hardware resources are performing useful work with minimal latency whenever possible.

The key to achieving the best performance lies in strategically combining these techniques. Each tool targets a specific aspect of the solution, and the most effective approach leverages their combined strengths. As the ML ecosystem matures, the focus is shifting towards tools that support a wider range of hardware.

Future Trends

The path to optimal AI performance involves a strategic combination of hand-optimized kernels, high-performance frameworks, optimized libraries, graph compilers, and runtime optimizers. As the ML ecosystem evolves, automation and broader hardware support will become increasingly important. The UXL Foundation’s work towards unification is a significant step in this direction, promising a more streamlined and efficient AI development and deployment landscape.

Automated Optimization

Future development environments like Mojo and MLIR-based IREE aim to automate more critical path optimization by generating code directly through compilers, reducing reliance on pre-generated kernels. This progression extends beyond critical kernels, extending the state of the art automatic generation of interconnection logic for the entire codebase.This approach eliminates the need for manual tuning and enables the creation of highly efficient and scalable ML systems.

Expanding Hardware Support

Creating a truly diverse hardware ecosystem will take time. Research is crucial to bridge the gap between architecture-specific technologies and compiler-generated code. With the ability to analyze broader operation windows, make intelligent decisions based on the characteristics of the target hardware and understand semantic context, these tools can unlock even greater optimization opportunities.

A More Unified and Open Future

With such a wide range of software technologies required by customers in the field of AI, there needs to be a solution to enabling all these approaches on processors that doesn’t require vast amounts of software development effort per-processor. Efforts like the UXL Foundation, IREE, and Triton are working towards creating open platforms to streamline the integration of AI software with diverse hardware. By consolidating compilers, languages, runtimes, and optimized libraries, these initiatives aim to reduce complexity and enable the fast-moving AI ecosystem to thrive.

Long-term trends and open questions

Open research topics include the limits on the ability of automation to convert a model to an optimized executable and the extent of diversity in the critical code paths. Is the development and deployment of advanced AI/ML compiler technology necessary for the long-term success of the AI/ML industry or is it a tool to help accelerate rapid AI/ML model experimentation during the current era of AI/ML model design? Will AI/ML models eventually stabilize to a few kernels that need to be written and optimized once for each hardware accelerator surrounded by a framework to optimally invoke them?

The AI ecosystem is currently fragmented, but ongoing efforts to bridge the gap between software and hardware offer hope for a more unified and streamlined future. By understanding the challenges and embracing emerging solutions, developers can unlock the full potential of AI across diverse hardware platforms.

Conclusion

This white paper not only guides developers through the tangled web of AI hardware and software but also highlights the importance of this integration in pushing the boundaries of what AI can achieve. By understanding the intricacies of the ecosystem, developers can make informed decisions, optimize performance, and drive innovation forward in their AI projects.

The AI ecosystem, characterized by a plethora of hardware accelerators and software frameworks, is integral to the rapid advancements in artificial intelligence today. Understanding this complex landscape is crucial for developers and researchers who seek to leverage the full potential of AI technologies. The diversity of AI accelerators—ranging from GPUs and TPUs to FPGAs and ASICs—coupled with the array of software frameworks such as PyTorch, TensorFlow, JAX, Pallas, and Triton, presents both opportunities and challenges. Each hardware-software combination offers unique advantages and requires specific optimizations to achieve optimal performance. By gaining a deep understanding of these components and their interactions, developers can make informed decisions that enhance the efficiency and effectiveness of their AI models, driving innovation across various domains.

Efforts to simplify this complexity are ongoing, with significant strides being made to create more seamless integration and compatibility within the AI ecosystem. Frameworks are continuously evolving to support new hardware features, while initiatives like ONNX and MLIR aim to provide standardized, cross-platform solutions that bridge the gaps between different technologies. Additionally, the development of specialized libraries, compilers, and optimization tools is making it easier for developers to harness the power of diverse accelerators without needing to delve into the intricate details of each hardware platform. These efforts are crucial in lowering the barriers to entry for AI development, enabling a broader range of innovators to contribute to and benefit from the advancements in AI technology.

Looking ahead, the future of AI development is poised for remarkable growth as both hardware and software continue to evolve. Emerging technologies, such as quantum computing and neuromorphic processors, hold the promise of further accelerating AI capabilities and unlocking new applications. Simultaneously, advancements in AI frameworks and optimization techniques will enhance the ability to leverage these new hardware innovations effectively. As the ecosystem becomes more integrated and user-friendly, the potential for AI to transform industries and solve complex problems will expand exponentially. By staying attuned to these developments and embracing the tools and strategies designed to navigate the evolving landscape, developers can remain at the forefront of AI innovation, driving the next wave of technological breakthroughs.

References

Biagio Peccerillo, Mirco Mannino, Andrea Mondelli, Sandro Bartolini,
A survey on hardware accelerators: Taxonomy, trends, challenges, and perspectives,
Journal of Systems Architecture,
Volume 129, 2022, 102561, ISSN 1383-7621,
doi.org/10.1016/j.sysarc.2022.102561
(www.sciencedirect.com/science/article/pii/S1383762122001138)

@inproceedings{10.1145/3315508.3329973,
author = {Tillet, Philippe and Kung, H. T. and Cox, David},
title = {Triton: an intermediate language and compiler for tiled neural network computations},
year = {2019},
isbn = {9781450367196},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {doi.org/10.1145/3315508.3329973},
doi = {10.1145/3315508.3329973},

XLA : Compiling Machine Learning for Peak Performance
author = Amit Sabne
year=2020
https://openxla.org/xla
https://mlir.llvm.org/
https://github.com/iree-org/iree-turbine
https://iree.dev/

R. Bi, T. Xu, M. Xu and E. Chen, “PaddlePaddle: A Production-Oriented Deep Learning Platform Facilitating the Competency of Enterprises,” 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Hainan, China, 2022, pp. 92-99, doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00046. keywords: {Deep learning;Industries;Visualization;Software architecture;Software algorithms;User experience;Mathematics;deep learning platform;PaddlePaddle;employ-ees development;technology growth},

@incollection{NEURIPS2019_9015,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\‘{e}-Buc and E. Fox and R. Garnett},
pages = {8024–8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}

https://sycl.tech/

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo,
Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis,
Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow,
Andrew Harp, Geoffrey Irving, Michael Isard, Rafal Jozefowicz, Yangqing Jia,
Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Mike Schuster,
Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Jonathon Shlens,
Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker,
Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas,
Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke,
Yuan Yu, and Xiaoqiang Zheng.
TensorFlow: Large-scale machine learning on heterogeneous systems,
2015. Software available from tensorflow.org.

---