Noise-Agnostic One-Shot Training and Retraining for Robust DNN Inferencing on Analog Compute-in-Memory Systems

Analog Compute-in-Memory (ACiM) architectures are a promising alternatives to traditional von Neumann-based systems for accelerating deep neural networks (DNNs), as they alleviate the memory bottleneck by performing in-situ matrixvector multiplications. However, the analog nature of computation in ACiM makes DNNs highly susceptible to noise and process variations. To mitigate the effects of analog noise, existing approaches rely on variation-aware or noise-aware training, retraining, or fine-tuning. These methods, however, are not scalable, as they require chip-specific retraining and typically involve separate training runs for different levels of noise tolerance. Moreover, they overlook the inherent fault tolerance of analog-to-digital converters (ADCs). To address these limitations, we propose a one-shot training and retraining strategy for robust DNN inferencing on ACiM platforms. Our method is guided by a detailed analysis of error propagation through ADCs, revealing that robustness can be enhanced by strategically reshaping the weight distribution to better align with ADC resilience characteristics. Simulation results and experimental results with fabricated chips show that the proposed method improves inferencing accuracy by $\mathbf{7 0 \%} \boldsymbol{-} \mathbf{9 0 \%}$ for ResNet-18 and DenseNet-121 under $\mathbf{7 0 \%}$ noise injection on CIFAR-10 and SVHN, and by $\mathbf{5 0 \%}$-80% for VGG-16 under $\mathbf{5 0 \%}$ noise. These gains are achieved with only a $5 \%$ energy overhead due to the modified weight distribution.