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<p>Non-volatile memory-based computing-in-memory (nvCIM) paradigm has been extensively studied to boost the energy efficiency of neural network accelerators in edge applications. However, the degradation of inference accuracy induced by the non-ideal characteristics across circuits, arrays, and devices is becoming a crucial issue. In this work, we establish a hardware characteristic behavior model to analyze the impact of nvCIM non-ideal characteristics on neural network accuracy. Then we propose a hardware aware training and weight mapping correction methods to mitigate inference accuracy degradation. Through simulation verification, about 95% inference accuracy degradation is recovered by adopting the proposed mitigation method for various non-ideal characteristics and various neural network models. The feasibility of the proposed method is further proved in an experimental example with a flash-based LeNet recognition system.</p>

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Mitigating methodology of hardware non-ideal characteristics for non-volatile memory based neural networks

  • 期刊论文

  • 发表日期：

    2025年1月13日

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  • Lixia Han,
  • Peng Huang,
  • Yijiao Wang,
  • Zheng Zhou,
  • Haozhang Yang,
  • Yiyang Chen,
  • Xiaoyan Liu,
  • Jinfeng Kang

摘要

Non-volatile memory-based computing-in-memory (nvCIM) paradigm has been extensively studied to boost the energy efficiency of neural network accelerators in edge applications. However, the degradation of inference accuracy induced by the non-ideal characteristics across circuits, arrays, and devices is becoming a crucial issue. In this work, we establish a hardware characteristic behavior model to analyze the impact of nvCIM non-ideal characteristics on neural network accuracy. Then we propose a hardware aware training and weight mapping correction methods to mitigate inference accuracy degradation. Through simulation verification, about 95% inference accuracy degradation is recovered by adopting the proposed mitigation method for various non-ideal characteristics and various neural network models. The feasibility of the proposed method is further proved in an experimental example with a flash-based LeNet recognition system.