A COMPARATIVE REVIEW OF FPGA AND GPU ACCELERATORS FOR AI

This investigation presents a brief yet thorough comparative assessment of FPGA (SoC)–based and GPU-based AI accelerators across applications that encompass edge devices to data center training environments. Primary performance parameters—latency, throughput, energy efficiency, programmability, and scalability—are thoroughly evaluated with a specific concentration on deep neural network inference and training. The research further highlights the significance of hardware/software co-design and high-level synthesis (HLS) in augmenting FPGA performance. Representative platforms, such as the one from Xilinx and Nvidia, are referenced to illustrate prevailing trends. Findings suggest that while GPUs excel in throughput and development simplicity, FPGAs exhibit reduced latency and enhanced energy efficiency in power-sensitive or real-time applications.