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Proceedings Paper

Digital neuromorphic chips for deep learning inference: a comprehensive study
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Paper Abstract

Over the past few years, deep neural networks have achieved state-of-the-art accuracy in a broad spectrum of applications. However, implementing deep networks in general purpose architectures is a challenging task as they require high computational resources and massive memory bandwidth. Recently, several digital neuromorphic chips have been proposed to address these issues. In this paper, we explore sixteen prominent rate based digital neuromorphic chip architectures, optimized primarily for inference. Specific focus is on: What is the motivation to design digital neuromorphic chips? Which optimizations play a key role in improving their performance? What are the main research trends in current generation chips?

Paper Details

Date Published: 6 September 2019
PDF: 12 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390C (6 September 2019); doi: 10.1117/12.2529407
Show Author Affiliations
Hamed F. Langroudi, Rochester Institute of Technology (United States)
Tej Pandit, Rochester Institute of Technology (United States)
Mark Indovina, Rochester Institute of Technology (United States)
Dhireesha Kudithipudi, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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