An Asynchronous Delta Modulator for Spike Encoding in Event-Driven Brain-Machine Interface

TL;DR

This paper introduces an asynchronous delta modulator for spike encoding in neural interfaces, enabling efficient, real-time brain-machine communication with low energy consumption and compact design.

Contribution

It presents a novel asynchronous spike encoder circuit for neural recording, optimized for integration with neuromorphic systems in BMI applications.

Findings

Energy consumption of 60.73 nJ per spike.

Achieved an F1-score of 80% in spike encoding accuracy.

Compact chip area of approximately 73.5 by 73.6 micrometers.

Abstract

This paper presents the design and implementation of an asynchronous delta modulator as a spike encoder for event-driven neural recording in a 65nm CMOS process. The proposed neuromorphic front-end converts analog signals into discrete, asynchronous ON and OFF spikes, effectively compressing continuous biopotentials into spike trains compatible with spiking neural networks (SNNs). Its asynchronous operation enables seamless integration with neuromorphic architectures for real-time decoding in closed-loop brain-machine interfaces (BMIs). Measurement results from silicon demonstrate an energy consumption of 60.73 nJ/spike, an F1-score of 80% compared to a behavioral model of the asynchronous delta modulator, and a compact pixel area of 73.45 um $\times$ 73.64 um.