Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85536
Title: An Energy-Efficient Digital ReRAM-Crossbar-Based CNN With Bitwise Parallelism
Authors: Ni, Leibin
Liu, Zichuan
Yu, Hao
Joshi, Rajiv V.
Keywords: Approximate computing
Neural network hardware
Issue Date: 2017
Source: Ni, L., Liu, Z., Yu, H., & Joshi, R. V. (2017). An Energy-Efficient Digital ReRAM-Crossbar-Based CNN With Bitwise Parallelism. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 3, 37-46.
Series/Report no.: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Abstract: There is great attention to develop hardware accelerator with better energy efficiency, as well as throughput, than GPUs for convolutional neural network (CNN). The existing solutions have relatively limited parallelism as well as large power consumption (including leakage power). In this paper, we present a resistive random access memory (ReRAM)-accelerated CNN that can achieve significantly higher throughput and energy efficiency when the CNN is trained with binary constraints on both weights and activations, and is further mapped on a digital ReRAM-crossbar. We propose an optimized accelerator architecture tailored for bitwise convolution that features massive parallelism with high energy efficiency. Numerical experiment results show that the binary CNN accelerator on a digital ReRAM-crossbar achieves a peak throughput of 792 GOPS at the power consumption of 4.5 mW, which is 1.61 times faster and 296 times more energy-efficient than a high-end GPU.
URI: https://hdl.handle.net/10356/85536
http://hdl.handle.net/10220/43795
DOI: 10.1109/JXCDC.2017.2697910
Schools: School of Electrical and Electronic Engineering 
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/JXCDC.2017.2697910].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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