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Analog in-memory computing can achieve high accuracy for deep learning tasks. Hardware-aware retraining enables neural networks to maintain performance despite hardware imperfections, especially for recurrent networks.

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Area of Science:

  • Hardware-aware machine learning
  • Energy-efficient computing architectures
  • Deep learning acceleration

Background:

  • Analog in-memory computing (AIMC) offers energy-efficient deep learning acceleration by performing matrix-vector multiplications.
  • Non-ideal device characteristics in AIMC can lead to non-deterministic or non-linear operations, potentially reducing inference accuracy.
  • Existing retraining methods may not fully account for the complex non-idealities present in analog hardware.

Purpose of the Study:

  • To develop a hardware-aware retraining approach for systematically evaluating analog in-memory computing accuracy.
  • To investigate the sensitivity and robustness of deep neural networks to various non-idealities in AIMC.
  • To demonstrate that deep neural networks can achieve comparable accuracy to floating-point implementations using AIMC.

Main Methods:

  • A hardware-aware retraining methodology was developed to optimize deep neural networks for AIMC.
  • A realistic crossbar model was integrated to simulate AIMC non-idealities.
  • Multiple network topologies, including convolutional neural networks (convnets), recurrent neural networks (RNNs), and transformers, were analyzed.
  • Sensitivity analysis was performed across a range of non-ideal conditions.

Main Results:

  • Many large-scale deep neural networks, including convnets, RNNs, and transformers, can be successfully retrained to achieve iso-accuracy compared to floating-point implementations.
  • Non-idealities affecting inputs or outputs have a greater impact on accuracy than those affecting weights.
  • Recurrent neural networks demonstrated particular robustness against all investigated non-idealities.

Conclusions:

  • Hardware-aware retraining is effective in mitigating accuracy degradation in analog in-memory computing for deep learning.
  • Input/output noise significantly impacts AIMC accuracy, highlighting areas for hardware and algorithm co-design.
  • Recurrent networks are a promising architecture for robust and accurate analog in-memory computing applications.