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MEMTONIC: a neuromorphic accelerator for energy efficient deep learning

Published: 18 November 2020 Publication History
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  • Abstract

    Most deep learning accelerators in the literature focus only on improving the design of inference phase. We propose a novel photonics-based backpropagation accelerator for high performance deep learning training. The proposed MEMTONIC architecture is a first-of-its-kind memristor-integrated photonics-based deep learning architecture for end-to-end training and prediction. We evaluate the architecture using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves at least 35× acceleration in training time, 31× improvement in computational efficiency, and 45× energy savings compared to the state-of-the-art designs, without any loss of accuracy.

    References

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    C. Zhang, et.al, "Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks", IEEE/ACM ICCAD, Nov. 2016.
    [2]
    A. Shafiee, et.al, "ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars", IEEE/ ISCA, Jun. 2016.
    [3]
    T. Gokmen and Y. Vlasov "Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations", Front. Neuroscience, July 2016.
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    L. Song, et.al, "PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning", IEEE HPCA, Feb. 2017.
    [5]
    Miao Hu, et.al, "Dot-Product Engine for Neuromorphic Computing: Programming 1T1M Crossbar to Accelerate Matrix-Vector Multiplication", IEEE/ACM DAC, 2016.
    [6]
    K Vandoorne, et.al, "Parallel Reservoir Computing Using Optical Amplifiers", IEEE Trans. Neural Networks, 2011.
    [7]
    Y. Shen, et.al, "Deep learning with coherent nanophotonic circuits", Nature Photonics, 2017.
    [8]
    IPKISS Design Framework: www.lucedaphotonics.com
    1. MEMTONIC: a neuromorphic accelerator for energy efficient deep learning

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      Published In

      cover image ACM Conferences
      DAC '20: Proceedings of the 57th ACM/EDAC/IEEE Design Automation Conference
      July 2020
      1545 pages
      ISBN:9781450367257
      • General Chair:
      • Zhuo Li

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      • IEEE-CEDA

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      IEEE Press

      Publication History

      Published: 18 November 2020

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      Author Tags

      1. deep learning
      2. memristor
      3. on-chip photonics

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      Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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      DAC '25
      62nd ACM/IEEE Design Automation Conference
      June 22 - 26, 2025
      San Francisco , CA , USA

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