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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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SNN eXpress: Streamlining Low-Power AI-SoC Development With Unsigned Weight Accumulation Spiking Neural Network

  • Hyeonguk Jang (AI Edge SoC Research Section, Electronics and Telecommunications Research Institute) ;
  • Kyuseung Han (AI Edge SoC Research Section, Electronics and Telecommunications Research Institute) ;
  • Kwang-Il Oh (AI Edge SoC Research Section, Electronics and Telecommunications Research Institute) ;
  • Sukho Lee (AI Edge SoC Research Section, Electronics and Telecommunications Research Institute) ;
  • Jae-Jin Lee (AI Edge SoC Research Section, Electronics and Telecommunications Research Institute) ;
  • Woojoo Lee (School of Electrical and Electronics Engineering, Chung-Ang University)
  • Received : 2024.03.14
  • Accepted : 2024.08.13
  • Published : 2024.10.10
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Abstract

SoCs with analog-circuit-based unsigned weight-accumulating spiking neural networks (UWA-SNNs) are a highly promising solution for achieving lowpower AI-SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA-SNNs in low-power AI-SoCs: (i) the absence of UWA-SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed-signal circuit implementation of UWA-SNN-based SoCs. This paper argues that, by integrating the proposed solutions, the development of an EDA tool that enables the easy and rapid development of UWA-SNN-based SoCs is feasible, and demonstrates this through the development of the SNN eXpress (SNX) tool. The developed SNX automates the generation of RTL code, FPGA prototypes, and a software development kit tailored for UWA-SNN-based application development. Comprehensive details of SNX development and the performance evaluation and verification results of two AI-SoCs developed using SNX are also presented.

Keywords

Acknowledgement

This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korea government (24ZS1230, memory-computation convergence neuromorphic computing technology).

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