FPGA's and AI - Frameworks, Architectures and Applications (Intel/Xilinx)

Artificial intelligence (AI) is the field of computer science that aims to create machines and systems that can perform tasks that normally require human intelligence, such as perception, reasoning, learning, decision making, and natural language processing. AI applications are becoming more widespread and diverse, ranging from self-driving cars and smart assistants to medical diagnosis and drug discovery.

However, AI also poses significant challenges in terms of computational complexity, power consumption, and scalability. Traditional CPU-based architectures are often limited by their sequential processing capabilities, high latency, and low energy efficiency. GPU-based architectures offer higher parallelism and performance, but they are still constrained by their fixed hardware design, high cost, and power consumption.

Field-programmable gate arrays (FPGAs) are a type of reconfigurable hardware that can be programmed to implement any logic function at the circuit level. FPGAs have several advantages over CPUs and GPUs for AI applications, such as flexibility,  performance and efficiency. 

Flexibility: 

FPGAs can be customized to match the specific requirements of different AI algorithms, such as data precision, memory hierarchy, and parallelism. FPGAs can also be dynamically reconfigured to adapt to changing workloads and environments.

Performance: 

FPGAs can achieve higher throughput and lower latency than CPUs and GPUs by exploiting the spatial parallelism and pipelining of hardware logic. FPGAs can also leverage specialized hardware accelerators, such as digital signal processors (DSPs), embedded memory blocks (EMBs), and high-speed transceivers (HSTs), to optimize the performance of specific AI tasks.

Efficiency: 

FPGAs can reduce the power consumption and cost of AI applications by eliminating the overhead of general-purpose processors, such as instruction fetching, decoding, scheduling, and caching. FPGAs can also operate at lower voltages and frequencies than CPUs and GPUs, resulting in lower energy dissipation.

One of the challenges of using FPGAs for AI applications is the difficulty of programming them. Unlike CPUs and GPUs, which have well-established software tools and libraries, FPGAs require low-level hardware description languages (HDLs), such as Verilog or VHDL, that are not intuitive or user-friendly for most AI developers. Moreover, FPGAs have limited compatibility with popular AI frameworks, such as TensorFlow or PyTorch, that provide high-level abstractions and functionalities for AI development.

To address this challenge, several FPGA-based AI frameworks and platforms have been developed in recent years that aim to simplify the design, programming, and deployment of FPGA-based AI solutions. Some examples are:

Xilinx Vitis AI: 

A software platform that enables users to develop and deploy FPGA-based AI applications using a unified programming model that supports both edge and cloud scenarios. Vitis AI provides a comprehensive set of tools and libraries that integrate with popular AI frameworks, such as TensorFlow or PyTorch, and enable users to optimize their AI models for FPGA acceleration.

Intel OpenVINO: 

A toolkit that helps users to accelerate their AI inference workloads on various Intel devices, including FPGAs. OpenVINO supports a wide range of AI models and use cases, such as computer vision, natural language processing, speech recognition, and more. OpenVINO also provides a model optimizer that converts AI models from different frameworks into an intermediate representation that can be executed on FPGAs.

Microsoft Project Brainwave: 

A cloud-based platform that enables users to run their AI models on large-scale FPGA clusters hosted on Microsoft Azure. Project Brainwave supports real-time AI inference for various domains, such as image recognition, natural language processing, machine translation, and more. Project Brainwave also provides a distributed system architecture that allows users to scale up their FPGA resources on demand.

One of the questions that may arise when choosing an FPGA-based AI framework or platform is: 

What is the difference between Xilinx Vitis AI and Intel OpenVINO? 

Here are some of the main differences:

Hardware support: 

Xilinx Vitis AI is designed to work with Xilinx FPGAs only, while Intel OpenVINO supports not only Intel FPGAs but also other Intel devices, such as CPUs, GPUs, VPUs (vision processing units), etc.

Software support: 

Xilinx Vitis AI integrates with TensorFlow or PyTorch directly through its tools and libraries, while Intel OpenVINO requires users to convert their models from different frameworks into an intermediate representation using its model optimizer tool.

Deployment scenarios: 

Xilinx Vitis AI supports both edge and cloud deployment scenarios for FPGA-based AI applications. Users can choose between different target platforms depending on their needs. For example, users can use Xilinx Zynq UltraScale+ MPSoC devices for edge applications or Xilinx Alveo cards for cloud applications. Intel OpenVINO also supports both edge and cloud scenarios but focuses more on edge applications. Users can use Intel Arria 10 or Stratix 10 FPGAs for edge applications or Intel Programmable Acceleration Cards (PACs) for cloud applications.

Microsoft Azure Machine Learning: 

This is a cloud-based service that enables users to build, train, and deploy machine learning models using FPGAs. The service leverages Microsoft's Project Brainwave, which is a platform that uses FPGAs to accelerate deep neural networks (DNNs), the core of many AI applications. The service allows users to access pre-trained models or create their own models using popular frameworks, such as TensorFlow and PyTorch. The service also supports real-time inference, which means that the models can process data streams with low latency and high throughput.