Compute-in-Memory (CiM) utilizing non-volatile memory (NVM) devices presents a highly promising and efficient approach for accelerating deep neural networks (DNNs). By concurrently storing network weights and performing matrix operations within the same crossbar structure, CiM accelerators offer DNN inference acceleration with minimal area requirements and exceptional energy efficiency. However, the stochasticity and intrinsic variations of NVM devices often lead to performance degradation, such as reduced classification accuracy, compared to expected outcomes. Although several methods have been proposed to mitigate device variation and enhance robustness, most of them rely on overall modulation and lack constraints on the training process. Drawing inspiration from the negative feedback mechanism, we introduce a novel training approach that uses a multi-exit mechanism as negative feedback to enhance the performance of DNN models in the presence of device variation. Our negative feedback training method surpasses state-of-the-art techniques by achieving an impressive improvement of up to 12.49% in addressing DNN robustness against device variation.

翻译：基于非易失性存储（NVM）器件的存内计算（CiM）为加速深度神经网络（DNN）提供了一种极具前景且高效的方案。通过在相同的交叉阵列结构中同时存储网络权重并执行矩阵运算，CiM加速器能以极小的面积需求和出色的能效实现DNN推理加速。然而，NVM器件的随机性和固有变异往往会导致性能下降，例如分类精度低于预期结果。尽管已有多种方法被提出以缓解器件变异并增强鲁棒性，但大多数方法依赖于整体调制，且缺乏对训练过程的约束。受负反馈机制的启发，我们引入了一种新颖的训练方法，该方法利用多出口机制作为负反馈，以在存在器件变异的情况下提升DNN模型的性能。我们的负反馈训练方法在解决DNN对器件变异的鲁棒性问题上，相较于现有最先进技术实现了高达12.49%的显著提升。