An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks

By Mohammad Javad Sekonji ¹, Ali Mahani ¹, Maryam Mirsadeghi ¹, and Mahdi Taheri ^2,3
1 Shahid Bahonar University of Kerman, Kerman, Iran
² Brandenburg Technical University, Cottbus, Germany
³ Tallinn University of Technology, Tallinn, Estonia

Abstract

Spiking Neural Networks (SNNs) offer high energy efficiency and event-driven computation, ideal for low-power edge AI. Their hardware implementation on FPGAs, however, faces challenges due to heavy computation, large memory use, and limited flexibility. This paper proposes a compact System-on-Chip (SoC) architecture for temporal-coding SNNs, integrating a RISC-V controller with an event-driven SNN core. It replaces multipliers with bitwise operations using binarized weights, includes a spike-time sorter for active spikes, and skips noninformative events to reduce computation. The architecture runs fully on a Xilinx Artix-7 FPGA, achieving up to 16x memory reduction for weights and lowering computational overhead and latency, with 97.0% accuracy on MNIST and 88.3% on FashionMNIST. This self-contained design provides an efficient, scalable platform for real-time neuromorphic inference at the edge.

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