On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise
<|reference_start|>On-Chip Learning with Memristor-Based Neural Networks: Assessing Accuracy and Efficiency Under Device Variations, Conductance Errors, and Input Noise: This paper presents a memristor-based compute-in-memory hardware accelerator for on-chip training and inference, focusing on its accuracy and efficiency against device variations, conductance errors, and input noise. Utilizing realistic SPICE models of commercially available silver-based metal self-directed channel (M-SDC) memristors, the study incorporates inherent device non-idealities into the circuit simulations. The hardware, consisting of 30 memristors and 4 neurons, utilizes three different M-SDC structures with tungsten, chromium, and carbon media to perform binary image classification tasks. An on-chip training algorithm precisely tunes memristor conductance to achieve target weights. Results show that incorporating moderate noise (<15%) during training enhances robustness to device variations and noisy input data, achieving up to 97% accuracy despite conductance variations and input noises. The network tolerates a 10% conductance error without significant accuracy loss. Notably, omitting the initial memristor reset pulse during training considerably reduces training time and energy consumption. The hardware designed with chromium-based memristors exhibits superior performance, achieving a training time of 2.4 seconds and an energy consumption of 18.9 mJ. This research provides insights for developing robust and energy-efficient memristor-based neural networks for on-chip learning in edge applications.<|reference_end|>
@article{eslami2024on-chip,
    title={On-Chip Learning with Memristor-Based Neural Networks: Assessing
  Accuracy and Efficiency Under Device Variations, Conductance Errors, and
  Input Noise},
    author={M. Reza Eslami, Dhiman Biswas, Soheib Takhtardeshir, Sarah S. Sharif,
  and Yaser M. Banad},
    journal={arXiv preprint arXiv:2408.14680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14680},
    primaryClass={cs.NE cond-mat.mtrl-sci cs.LG}
}