NDLI: RENO: A high-efficient reconfigurable neuromorphic computing accelerator design

Content Provider	IEEE Xplore Digital Library
Author	Xiaoxiao Liu Mengjie Mao Beiye Liu Hai Li Yiran Chen Boxun Li Yu Wang Hao Jiang Barnell, M. Qing Wu Jianhua Yang
Copyright Year	2015
Description	Author affiliation: Univ. of Massachusetts, Amherst, MA, USA (Jianhua Yang) \|\| Air Force Res. Lab., Rome, NY, USA (Barnell, M.; Qing Wu) \|\| Univ. of Pittsburgh, Pittsburgh, PA, USA (Xiaoxiao Liu; Mengjie Mao; Beiye Liu; Hai Li; Yiran Chen) \|\| Tsinghua Univ., Beijing, China (Boxun Li; Yu Wang) \|\| San Francisco State Univ., San Francisco, CA, USA (Hao Jiang)
Abstract	Neuromorphic computing is recently gaining significant attention as a promising candidate to conquer the well-known von Neumann bottleneck. In this work, we propose RENO - a efficient reconfigurable neuromorphic computing accelerator. RENO leverages the extremely efficient mixed-signal computation capability of memristor-based crossbar (MBC) arrays to speedup the executions of artificial neural networks (ANNs). The hierarchically arranged MBC arrays can be configured to a variety of ANN topologies through a mixed-signal interconnection network (M-Net). Simulation results on seven ANN applications show that compared to the baseline general-purpose processor, RENO can achieve on average 178.4× (27.06×) performance speedup and 184.2× (25.23×) energy savings in high-efficient multilayer perception (high-accurate auto-associative memory) implementation. Moreover, in the comparison to a pure digital neural processing unit (D-NPU) and a design with MBC arrays co-operating through a digital interconnection network, RENO still achieves the fastest execution time and the lowest energy consumption with similar computation accuracy.
Starting Page	1
Ending Page	6
File Size	903014
Page Count	6
File Format	PDF
ISBN	9781479980529
DOI	10.1145/2744769.2744900
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Active appearance model Artificial neural networks Training Arrays Routing Accuracy Memristors
Content Type	Text
Resource Type	Article

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