SNAP-V: A RISC-V SoC with Configurable Neuromorphic Acceleration for Small-Scale Spiking Neural Networks
It addresses the problem of energy-efficient, real-time SNN inference for edge computing, though it is incremental as it builds on existing RISC-V and neuromorphic concepts.
The paper tackles the inefficiency of existing hardware for small-scale Spiking Neural Networks (SNNs) by introducing SNAP-V, a RISC-V-based SoC with configurable neuromorphic accelerators, achieving an average accuracy deviation of 2.62% and synaptic energy of 1.05 pJ per operation.
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.