Hardware-software co-design for efficient and scalable real-time emulation of SNNs on the edge

This paper introduces a novel workflow for Distributed Spiking Neural Network Architecture (DSNA). As such, the hardware implementation of Single Instruction Multiple Data (SIMD)-based Spiking Neural Network (SNN) requires the development of user-friendly and efficient toolchain in order to maximise the potential that the architecture brings. By using a novel SNN architecture, a custom designed hardware/software toolchain has been developed. The toolchain performance has been experimentally checked on a Band-Pass Filter (BPF), obtaining optimized code and data. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.