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Published in:
Introduction to Stochastic Computing Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Brain-Inspired Computing

Authors : Naoya Onizawa, Warren J. Gross, Takahiro Hanyu

Published in: Stochastic Computing: Techniques and Applications

Publisher: Springer International Publishing

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Abstract

This chapter summarizes applications of stochastic computing for brain-inspired 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.

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previous chapter Spintronic Solutions for Stochastic Computing
next chapter Stochastic Decoding of Error-Correcting Codes
Footnotes
1
Since 2014 in BLSI project of Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan, we have implemented several BLSIs based on stochastic computing for brain-inspired physiological models and deep neural networks.
 
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Metadata
Title
Brain-Inspired Computing
Authors
Naoya Onizawa
Warren J. Gross
Takahiro Hanyu
Copyright Year
2019
Book
Stochastic Computing: Techniques and Applications

Print ISBN: 978-3-030-03729-1

Electronic ISBN: 978-3-030-03730-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-03730-7_10