Hardware implementation of FPGA-based spiking attention neural network accelerator

Abstract

Spiking neural networks (SNNs) are recognized as third-generation neural networks and have garnered significant attention due to their biological plausibility and energy efficiency. To address the resource constraints associated with using field programmable gate arrays (FPGAs) for numerical recognition in SNNs, we proposed a lightweight spiking efficient attention neural network (SeaSNN) accelerator. We designed a simple, four-layer structured network, achieving a recognition accuracy of 93.73% through software testing on the MNIST dataset. To further enhance the model’s accuracy, we developed a highly spiking efficient channel attention mechanism (SECA), resulting in a significant performance improvement and an increase in test accuracy to 94.28%. For higher recognition speed, we optimized circuit parallelism by applying techniques such as loop unrolling, loop pipelining, and array partitioning. Finally, SeaSNN was implemented and verified on an FPGA board, achieving an inference speed of 0.000401 seconds per frame and a power efficiency of 0.42 TOPS/W at a frequency of 200 MHz. These results demonstrate that the proposed low-power, high-precision, and fast handwritten digit recognition system is well-suited for handwritten digit recognition tasks.