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

This comprehensive review examines FPGA-based AI accelerators as a promising solution for deep learning workloads, addressing the limitations of ASIC and GPU accelerators. The paper analyzes hardware optimizations including loop pipelining, parallelism, and quantization techniques that make FPGAs attractive for AI applications requiring high performance and energy efficiency.

• →FPGAs offer flexible and reconfigurable platforms for deep learning acceleration, providing advantages over ASIC and GPU solutions.
• →Hardware optimizations like loop pipelining, parallelism, and quantization are key techniques for improving FPGA-based AI accelerator performance.
• →Growing complexity of deep learning models demands substantial computational power and memory bandwidth, driving need for specialized hardware.
• →FPGA-based accelerators enable model-specific customization while maintaining high energy efficiency.
• →The research identifies ongoing challenges that present opportunities for future innovations in FPGA-based AI hardware design.