Spiking Neural Networks (SNNs) have gained significant attention in edge computing due to their low power consumption and computational efficiency. However, existing implementations either use conventional System on Chip (SoC) architectures that suffer from memory-processor bottlenecks, or large-scale neuromorphic hardware that is inefficient and wasteful for small-scale SNN applications. This work presents SNAP-V, a RISC-V-based neuromorphic SoC with two accelerator variants: Cerebra-S (bus-based) and Cerebra-H (Network-on-Chip (NoC)-based) which are optimized for small-scale SNN inference, integrating a RISC-V core for management tasks, with both accelerators featuring parallel processing nodes and distributed memory. Experimental results show close agreement between software and hardware inference, with an average accuracy deviation of 2.62% across multiple network configurations, and an average synaptic energy of 1.05 pJ per synaptic operation (SOP) in 45 nm CMOS technology. These results show that the proposed solution enables accurate, energy-efficient SNN inference suitable for real-time edge applications.

翻译：脉冲神经网络（SNNs）因其低功耗和计算效率在边缘计算领域受到广泛关注。然而，现有实现方案要么采用传统片上系统（SoC）架构而受限于内存-处理器瓶颈，要么使用大规模神经形态硬件，这对于小规模SNN应用而言效率低下且资源浪费。本文提出SNAP-V，一种基于RISC-V的神经形态SoC，包含两种加速器变体：基于总线的Cerebra-S和基于片上网络（NoC）的Cerebra-H。该系统针对小规模SNN推理进行优化，集成RISC-V内核处理管理任务，两种加速器均具备并行处理节点和分布式内存。实验结果表明，软件与硬件推理结果高度吻合，在多种网络配置下平均精度偏差为2.62%，采用45纳米CMOS工艺时每次突触操作（SOP）的平均突触能耗为1.05皮焦。这些结果证明，所提出的解决方案能够实现精确、高能效的SNN推理，适用于实时边缘应用。