Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review

TL;DR

This comprehensive review discusses FPGA-based AI accelerators, highlighting their advantages, hardware optimizations, and current challenges to improve deep learning performance and energy efficiency.

Contribution

It provides an extensive overview of FPGA-based neural network accelerators, including hardware optimizations and challenges, advancing understanding of FPGA's role in AI hardware design.

Findings

FPGA accelerators offer flexible, high-efficiency solutions for deep learning.

Hardware optimizations like pipelining and quantization improve performance.

Identified challenges guide future FPGA-based AI accelerator development.

Abstract

Deep learning (DL) has emerged as a rapidly developing advanced technology, enabling the performance of complex tasks involving image recognition, natural language processing, and autonomous decision-making with high levels of accuracy. However, as these technologies evolve and strive to meet the growing demands of real-life applications, the complexity of DL models continues to increase. These models require processing of massive volumes of data, demanding substantial computational power and memory bandwidth. This gives rise to the critical need for hardware accelerators that can deliver both high performance and energy efficiency. Accelerator types include ASIC based solutions, GPU accelerators, and FPGA based implementations. The limitations of ASIC and GPU accelerators have led to FPGAs becoming one of the prominent solutions, offering distinct advantages for DL workloads. FPGAs provide a flexible and reconfigurable platform, allowing model specific customization while maintaining high efficiency. This article explores various hardware level optimizations for DL. These optimizations include techniques such as loop pipelining, parallelism, quantization, and various memory hierarchy enhancements. In addition, it provides an overview of state-of-the-art FPGA-based neural network accelerators. Through the study and analysis of these accelerators, several challenges have been identified, paving the way for future optimizations and innovations in the design of FPGA-based hardware accelerators.