A neuromorphic neural spike clustering processor for deep-brain sensing and stimulation systems

Beinuo Zhang, Zhewei Jiang, Qi Wang, Jae-sun Seo, Mingoo Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the International Symposium on Low Power Electronics and Design
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages91-97
Number of pages7
Volume2015-September
ISBN (Print)9781467380096
DOIs
StatePublished - Sep 21 2015
Event20th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2015 - Rome, Italy
Duration: Jul 22 2015Jul 24 2015

Other

Other20th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2015
Country/TerritoryItaly
CityRome
Period7/22/157/24/15

Keywords

  • Accuracy
  • Clustering algorithms
  • Encoding
  • Firing
  • Hardware
  • Neurons
  • Training

ASJC Scopus subject areas

  • Engineering(all)

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