ReSCom: A Reconfigurable Spiking Neural Network Accelerator Using Stochastic Computing

ReSCom addresses the high power and area costs of Spiking Neural Network (SNN) hardware by introducing a reconfigurable accelerator that uses stochastic computing for multiplication while preserving exact fixed-point addition and subtraction. The method employs a unified neuron design supporting IF, LIF, and Synaptic models, enabling runtime trade-offs between accuracy, latency, and energy. On MNIST inference with a Xilinx Artix-7 FPGA, ReSCom achieves 92.80% accuracy at 0.05 mJ per image and 100 MHz, outperforming recent state-of-the-art implementations in energy efficiency. This matters because it demonstrates that stochastic computing can stabilize SNN inference while providing explicit, dynamic control over accuracy-latency-energy trade-offs for resource-constrained edge applications.