OECS: a deep convolutional neural network accelerator based on a 3D hybrid optical-electrical NoC
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Published:2023-10-16
Issue:11
Volume:15
Page:839
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ISSN:1943-0620
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Container-title:Journal of Optical Communications and Networking
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language:en
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Short-container-title:J. Opt. Commun. Netw.
Author:
Zhang Bowen1,
Gu Huaxi1,
Wang Kun1,
Yang Yintang1,
Ma Ziteng1
Abstract
Various emerging technologies have been proposed and applied to deep convolutional neural networks (DCNNs) to enhance their execution efficiency and accuracy while reducing the number of parameters. However, previous dataflow-based DCNN accelerators have primarily focused on accelerating the convolution (CONV) layer while reducing the overhead of data movement through specific data reuse strategies to improve performance. When deploying these emerging DCNNs in conventional accelerators, frequent data access from both the external memory and internal processing units of the accelerator can result in significant data movement overhead, leading to decreased acceleration performance. To address these challenges, this paper proposes a novel three-dimensional hybrid optical-electrical network-on-chip (NoC) accelerator called the optical-electronic channel-stationary system (OECS). OECS leverages a channel stationary (CS) calculation mode and stay-at-local data storage strategy to minimize the movement and processing of all data in the local processing element (PE). This strategy reduces data movement between each PE in the accelerator and between the accelerator and external memory, resulting in improved acceleration performance. Simulation results demonstrate that, compared to state-of-the-art accelerators, OECS achieves a 53% improvement in execution speed and saves approximately 44% of energy consumption associated with data movement.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province for Distinguished Young Scholars
The Youth Innovation Team of Shaanxi Universities
Publisher
Optica Publishing Group
Subject
Computer Networks and Communications