@inproceedings{32515d7c4ed54f508cdeadec0b48e4af,
title = "A neuromorphic neural spike clustering processor for deep-brain sensing and stimulation systems",
abstract = "This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas the conventional hardware based on the Osort algorithm achieves 69% for the same datasets. Implemented in a 65nm high-Vth, the processor exhibits a footprint of 0.25mm2/ch. and a power consumption of 9.3μW/ch. at VDD of 0.3V.",
keywords = "Accuracy, Clustering algorithms, Encoding, Firing, Hardware, Neurons, Training",
author = "Beinuo Zhang and Zhewei Jiang and Qi Wang and Jae-sun Seo and Mingoo Seok",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 20th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2015 ; Conference date: 22-07-2015 Through 24-07-2015",
year = "2015",
month = sep,
day = "21",
doi = "10.1109/ISLPED.2015.7273496",
language = "English (US)",
series = "Proceedings of the International Symposium on Low Power Electronics and Design",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "91--97",
booktitle = "Proceedings of the International Symposium on Low Power Electronics and Design, ISLPED 2015",
}