Efficient Hardware Acceleration of Spiking Neural Networks using FPGA: Towards Real-Time Edge Neuromorphic Computing

This paper examines the critical function of Field-Programmable Gate Arrays (FPGAs) in speeding Spiking Neural Networks (SNNs) for real-time edge neuromorphic computing. Our work systematically evaluates the integration of FPGA technology for the optimization and speeding of SNN models. The analysis covers the power efficiency, low latency processing, and parallelism that are intrinsic benefits of FPGAs, emphasizing their relevance for edge computing applications. We discuss the smooth transfer of trained SNN models to FPGA platforms. Using an extensive analysis of state-of-the-art architectures, we demonstrate the efficiency benefits of using FPGA to accelerate SNNs. We derive more insights into the real-world applications of this FPGA-SNN integration in various fields. The analysis supports advances in edge computing and neuromorphic processing paradigms by adding to the collective knowledge of how FPGA enhances the real-time processing capabilities of Spiking Neural Networks.