Extending Straight-Through Estimation for Robust Neural Networks on Analog CIM Hardware

--Analog Compute-In-Memory (CIM) architectures promise significant energy efficiency gains for neural network inference, but suffer from complex hardware-induced noise that poses major challenges for deployment. While noise-aware training methods have been proposed to address this issue, they typically rely on idealized and differentiable noise models that fail to capture the full complexity of analog CIM hardware variations. We provide theoretical analysis demonstrating that our approach preserves essential gradient directional information while maintaining computational tractability and optimization stability. Extensive experiments show that our extended STE framework achieves up to 5.3% accuracy improvement on image classification, 0.72 perplexity reduction on text generation, 2.2 speedup in training time, and 37.9% lower peak memory usage compared to standard noise-aware training methods. The exponential growth of neural network applications has intensified demand for energy-efficient computing solutions, particularly for edge devices with severe power and computational constraints [1], [2]. Analog Compute-In-Memory (CIM) architectures address these challenges by performing matrix-vector multiplications directly within memory arrays, eliminating energy-intensive data movement and achieving orders of magnitude energy efficiency improvements over traditional von Neumann architectures through analog weight storage and physical law-based computation [3], [4].