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RENO: A high-efficient reconfigurable neuromorphic computing accelerator design

Publication ,  Conference
Liu, X; Mao, M; Liu, B; Li, H; Chen, Y; Li, B; Wang, Y; Jiang, H; Barnell, M; Wu, Q; Yang, J
Published in: Proceedings - Design Automation Conference
July 24, 2015

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.

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Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

ISBN

9781450335201

Publication Date

July 24, 2015

Volume

2015-July
 

Citation

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Liu, X., Mao, M., Liu, B., Li, H., Chen, Y., Li, B., … Yang, J. (2015). RENO: A high-efficient reconfigurable neuromorphic computing accelerator design. In Proceedings - Design Automation Conference (Vol. 2015-July). https://doi.org/10.1145/2744769.2744900
Liu, X., M. Mao, B. Liu, H. Li, Y. Chen, B. Li, Y. Wang, et al. “RENO: A high-efficient reconfigurable neuromorphic computing accelerator design.” In Proceedings - Design Automation Conference, Vol. 2015-July, 2015. https://doi.org/10.1145/2744769.2744900.
Liu X, Mao M, Liu B, Li H, Chen Y, Li B, et al. RENO: A high-efficient reconfigurable neuromorphic computing accelerator design. In: Proceedings - Design Automation Conference. 2015.
Liu, X., et al. “RENO: A high-efficient reconfigurable neuromorphic computing accelerator design.” Proceedings - Design Automation Conference, vol. 2015-July, 2015. Scopus, doi:10.1145/2744769.2744900.
Liu X, Mao M, Liu B, Li H, Chen Y, Li B, Wang Y, Jiang H, Barnell M, Wu Q, Yang J. RENO: A high-efficient reconfigurable neuromorphic computing accelerator design. Proceedings - Design Automation Conference. 2015.

Published In

Proceedings - Design Automation Conference

DOI

ISSN

0738-100X

ISBN

9781450335201

Publication Date

July 24, 2015

Volume

2015-July