Brain-inspired computing

Naoya Onizawa, Warren J. Gross, Takahiro Hanyu

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This chapter summarizes applications of stochastic computing for braininspired computing, which we refer to as Brainware Large-Scale Integration (BLSI). Stochastic computing exploits random bit streams, realizing area-efficient hardware for complicated functions such as multiplication and tanh, as compared with more traditional binary approaches. Using stochastic computing, we have implemented hardware for several physiological models of the primary visual cortex of brains, where these models require such complicated functions. In addition, a deep neural network using stochastic computing has been designed for area/energy-efficient hardware. In order to design BLSIs, we have introduced extended arithmetic functions, such as circular functions. As a design example, our BLSIs are implemented using Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm Complementary Metal Oxide Semiconductor (CMOS) and discussed with traditional fixed-point implementations in terms of hardware performance and computation accuracy.

Original languageEnglish
Title of host publicationStochastic Computing
Subtitle of host publicationTechniques and Applications
PublisherSpringer International Publishing
Pages185-199
Number of pages15
ISBN (Electronic)9783030037307
ISBN (Print)9783030037291
DOIs
Publication statusPublished - 2019 Feb 18

Keywords

  • Deep neural networks
  • Integrated circuits
  • Neuromorphic computing

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)
  • Mathematics(all)

Fingerprint Dive into the research topics of 'Brain-inspired computing'. Together they form a unique fingerprint.

Cite this