1.74-µW/ch, 95.3%-accurate spike-sorting hardware based on Bayesian decision
Zhewei Jiang, Joao Pedro Cerqueira, Seongjong Kim, Qi Wang, Mingoo Seok · 2016
This paper presents algorithm/hardware co-design for real-time unsupervised spike sorting hardware for reducing power and improving sorting accuracy. We devise an algorithm based on Bayesian decision, which enables high accuracy while using noisy and simple time-domain features. Those simple features significantly reduce computation complexity, memory requirement, and thus the required number of cycles per sorting. The latter, coupled with the sparsity of spikes in time, makes the hardware idle for most of time, and thus we employ aggressive power gating and balloon latches to sleep most of the circuits and wake them up only when a spike is detected for maximal power savings. The hardware prototyped in a 65nm achieves higher accuracy at lower power than the existing arts.