Memristive stochastic computing for deep learning parameter optimization
Journal Publication ResearchOnline@JCUAbstract
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital logic. In contrast to conventional representation schemes used within the binary domain, the sequence of bit streams in the stochastic domain is inconsequential, and computation is usually non-deterministic. In this brief, we exploit the stochasticity during switching of probabilistic Conductive Bridging RAM (CBRAM) devices to efficiently generate stochastic bit streams in order to perform Deep Learning (DL) parameter optimization, reducing the size of Multiply and Accumulate (MAC) units by 5 orders of magnitude. We demonstrate that in using a 40-nm Complementary Metal Oxide Semiconductor (CMOS) process our scalable architecture occupies 1.55mm 2 and consumes approximately 167 μW when optimizing parameters of a Convolutional Neural Network (CNN) while it is being trained for a character recognition task, observing no notable reduction in accuracy post-training.
Journal
IEEE Transactions on Circuits and Systems II: Express Briefs
Publication Name
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Volume
68
ISBN/ISSN
1558-3791
Edition
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Issue
5
Pages Count
5
Location
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Publisher
Institute of Electrical and Electronics Engineers
Publisher Url
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Publisher Location
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Publish Date
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Url
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Date
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EISSN
N/A
DOI
10.1109/TCSII.2021.3065932