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A time-to-first-spike coding and conversion aware training for energy-efficient deep spiking neural network processor design

Published: 23 August 2022 Publication History

Abstract

In this paper, we present an energy-efficient SNN architecture, which can seamlessly run deep spiking neural networks (SNNs) with improved accuracy. First, we propose a conversion aware training (CAT) to reduce ANN-to-SNN conversion loss without hardware implementation overhead. In the proposed CAT, the activation function developed for simulating SNN during ANN training, is efficiently exploited to reduce the data representation error after conversion. Based on the CAT technique, we also present a time-to-first-spike coding that allows lightweight logarithmic computation by utilizing spike time information. The SNN processor design that supports the proposed techniques has been implemented using 28nm CMOS process. The processor achieves the top-1 accuracies of 91.7%, 67.9% and 57.4% with inference energy of 486.7uJ, 503.6uJ, and 1426uJ to process CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, when running VGG-16 with 5bit logarithmic weights.

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Cited By

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  • (2024)COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00083(1090-1106)Online publication date: 2-Nov-2024
  • (2024)Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC64144.2024.00074(413-420)Online publication date: 16-Dec-2024
  • (2024)Stellar: Energy-Efficient and Low-Latency SNN Algorithm and Hardware Co-Design with Spatiotemporal Computation2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00023(172-185)Online publication date: 2-Mar-2024
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cover image ACM Conferences
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
July 2022
1462 pages
ISBN:9781450391429
DOI:10.1145/3489517
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 August 2022

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Author Tags

  1. ANN-to-SNN conversion
  2. logarithmic computations
  3. spiking neural network
  4. temporal coding

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DAC '22: 59th ACM/IEEE Design Automation Conference
July 10 - 14, 2022
California, San Francisco

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

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Cited By

View all
  • (2024)COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00083(1090-1106)Online publication date: 2-Nov-2024
  • (2024)Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)10.1109/MCSoC64144.2024.00074(413-420)Online publication date: 16-Dec-2024
  • (2024)Stellar: Energy-Efficient and Low-Latency SNN Algorithm and Hardware Co-Design with Spatiotemporal Computation2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00023(172-185)Online publication date: 2-Mar-2024
  • (2023)Parallel spiking neurons with high efficiency and ability to learn long-term dependenciesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668457(53674-53687)Online publication date: 10-Dec-2023
  • (2023)A TTFS-based energy and utilization efficient neuromorphic CNN acceleratorFrontiers in Neuroscience10.3389/fnins.2023.112159217Online publication date: 5-May-2023
  • (2023)SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligenceScience Advances10.1126/sciadv.adi14809:40Online publication date: 6-Oct-2023
  • (2023)NBSSN: A Neuromorphic Binary Single-Spike Neural Network for Efficient Edge Intelligence2023 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS46773.2023.10181850(1-5)Online publication date: 21-May-2023
  • (2023)Towards Effective Training of Robust Spiking Recurrent Neural Networks Under General Input Noise via Provable Analysis2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323789(1-9)Online publication date: 28-Oct-2023

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