A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications

TL;DR

This paper introduces a hardware-efficient implementation of the Spiketrum algorithm for spike encoding in SNNs, reducing resource usage while maintaining effectiveness in real-world applications like sound and ECG classification.

Contribution

It presents an area-optimized hardware design for the Spiketrum algorithm, significantly reducing hardware resources compared to previous designs, and demonstrates its application in real-life neuromorphic systems.

Findings

52% reduction in Block RAMs

31% fewer DSP slices

Effective in sound and ECG classification

Abstract

Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresses the need for adaptable and resource-efficient spike encoding hardware by presenting an area-optimized hardware implementation of the Spiketrum algorithm, which encodes time-varying analogue signals into spatiotemporal spike patterns. Unlike earlier performance-optimized designs, which prioritize speed, our approach focuses on reducing hardware footprint, achieving a 52% reduction in Block RAMs (BRAMs), 31% fewer Digital Signal Processing (DSP) slices, and a 6% decrease in Look-Up Tables (LUTs). The proposed implementation has been verified on an FPGA and successfully integrated into an IC using TSMC180 technology. Experimental results demonstrate the system's effectiveness in real-world applications, including sound and ECG classification. This work highlights the trade-offs between performance and resource efficiency, offering a flexible, scalable solution for neuromorphic systems in power-sensitive applications like cochlear implants and neural devices.