Compute-in-memory (CIM) accelerators built upon non-volatile memory (NVM) devices excel in energy efficiency and latency when performing Deep Neural Network (DNN) inference, thanks to their in-situ data processing capability. However, the stochastic nature and intrinsic variations of NVM devices often result in performance degradation in DNN inference. Introducing these non-ideal device behaviors during DNN training enhances robustness, but drawbacks include limited accuracy improvement, reduced prediction confidence, and convergence issues. This arises from a mismatch between the deterministic training and non-deterministic device variations, as such training, though considering variations, relies solely on the model's final output. In this work, we draw inspiration from the control theory and propose a novel training concept: Negative Feedback Training (NFT) leveraging the multi-scale noisy information captured from network. We develop two specific NFT instances, Oriented Variational Forward (OVF) and Intermediate Representation Snapshot (IRS). Extensive experiments show that our methods outperform existing state-of-the-art methods with up to a 46.71% improvement in inference accuracy while reducing epistemic uncertainty, boosting output confidence, and improving convergence probability. Their effectiveness highlights the generality and practicality of our NFT concept in enhancing DNN robustness against device variations.

翻译：基于非易失性存储器（NVM）器件的存内计算（CIM）加速器凭借其原位数据处理能力，在执行深度神经网络（DNN）推理时具有卓越的能效和低延迟优势。然而，NVM器件的随机性与固有变异性常导致DNN推理性能下降。在DNN训练过程中引入这些非理想器件行为虽能增强鲁棒性，但仍存在精度提升有限、预测置信度降低及收敛困难等缺陷。这是由于确定性训练与非确定性器件变异性之间存在失配——此类训练虽考虑了变异性，却仅依赖模型的最终输出。本研究从控制理论中汲取灵感，提出一种新颖的训练概念：负反馈训练（NFT），利用从网络中捕获的多尺度噪声信息。我们开发了两种具体的NFT实例：定向变分前馈（OVF）与中间表征快照（IRS）。大量实验表明，我们的方法相较于现有最优方法，在降低认知不确定性、提升输出置信度及改善收敛概率的同时，推理精度最高可提升46.71%。其有效性凸显了NFT概念在增强DNN对器件变异鲁棒性方面的通用性与实用性。