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）器件的存算一体（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）推理时在能效和延迟方面表现优异。然而，NVM器件的随机特性和固有变化常导致DNN推理性能退化。在DNN训练过程中引入这些非理想器件行为可增强鲁棒性，但存在精度提升有限、预测置信度降低以及收敛问题等缺陷。这源于确定性训练与非确定性器件变化之间的失配——因为此类训练虽考虑了变化，却仅依赖于模型的最终输出。受控制理论启发，本文提出一种新型训练概念：负反馈训练（NFT），该概念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：定向变分前向（OVF）与中间表示快照（IRS）。大量实验表明，我们的方法在推理精度上相较于现有最优方法最高提升46.71%，同时能降低认知不确定性、提升输出置信度并改善收敛概率。这些方法的有效性凸显了NFT概念在增强DNN对器件变化的鲁棒性方面所具有的普适性和实用性。