A 2.6 TOPS/W 16-Bit Fixed-Point Convolutional Neural Network Learning Processor in 65-nm CMOS

Shihui Yin; Jae Sun Seo

doi:10.1109/LSSC.2019.2954780

A 2.6 TOPS/W 16-Bit Fixed-Point Convolutional Neural Network Learning Processor in 65-nm CMOS

Shihui Yin, Jae Sun Seo

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

We present a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with a momentum-based training algorithm in 16-bit fixed-point precision. Using a new cyclic weight storage and access scheme, we use the same off-the-shelf SRAMs for nontranspose and transpose operations during feedforward (FF) and feedbackward (FB) phases, respectively, of the CNN learning process. The 65-nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for 16-bit fixed-point operations, consuming 10.45 mW at 0.55 V.

Original language	English (US)
Article number	8907458
Pages (from-to)	13-16
Number of pages	4
Journal	IEEE Solid-State Circuits Letters
Volume	3
Issue number	1
DOIs	https://doi.org/10.1109/LSSC.2019.2954780
State	Published - Dec 2020

Keywords

Convolutional neural networks (CNNs)
dual-read-mode weight storage
on-chip learning
stochastic gradient descent (SGD)

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/LSSC.2019.2954780

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abstract = "We present a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with a momentum-based training algorithm in 16-bit fixed-point precision. Using a new cyclic weight storage and access scheme, we use the same off-the-shelf SRAMs for nontranspose and transpose operations during feedforward (FF) and feedbackward (FB) phases, respectively, of the CNN learning process. The 65-nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for 16-bit fixed-point operations, consuming 10.45 mW at 0.55 V.",

keywords = "Convolutional neural networks (CNNs), dual-read-mode weight storage, on-chip learning, stochastic gradient descent (SGD)",

author = "Shihui Yin and Seo, {Jae Sun}",

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AB - We present a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with a momentum-based training algorithm in 16-bit fixed-point precision. Using a new cyclic weight storage and access scheme, we use the same off-the-shelf SRAMs for nontranspose and transpose operations during feedforward (FF) and feedbackward (FB) phases, respectively, of the CNN learning process. The 65-nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for 16-bit fixed-point operations, consuming 10.45 mW at 0.55 V.

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KW - dual-read-mode weight storage

KW - on-chip learning

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A 2.6 TOPS/W 16-Bit Fixed-Point Convolutional Neural Network Learning Processor in 65-nm CMOS

Abstract

Keywords

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