A Low-Power Spike Detector Using In-Memory Computing for Event-based Neural Frontend

Autor:	Ke, Ye, Basu, Arindam
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	With the sensor scaling of next-generation Brain-Machine Interface (BMI) systems, the massive A/D conversion and analog multiplexing at the neural frontend poses a challenge in terms of power and data rates for wireless and implantable BMIs. While previous works have reported the neuromorphic compression of neural signal, further compression requires integration of spike detectors on chip. In this work, we propose an efficient HRAM-based spike detector using In-memory computing for compressive event-based neural frontend. Our proposed method involves detecting spikes from event pulses without reconstructing the signal and uses a 10T hybrid in-memory computing bitcell for the accumulation and thresholding operations. We show that our method ensures a spike detection accuracy of 92-99% for neural signal inputs while consuming only 13.8 nW per channel in 65 nm CMOS. Comment: Originally submitted at IEEE ISCAS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.08428 Zobrazit plný text záznamu View this record from Arxiv