Kaushik Lakshmiramanan, Vineeta Nair, Ching-Yi Lin et al.
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.