Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks

Memristive computing offers a promising route toward energy‐efficient neural inference, yet its practical efficiency is limited by intensive crossbar computation and the wide output current range required by analog‐to‐digital converters (ADCs). Here, an energy‐aware perturbation framework is presented to jointly alleviate these bottlenecks. The proposed method combines sinusoidal perturbation encoding with dual‐threshold screening to reshape layer inputs, suppress redundancy‐dominated activations, and reduce both pulse‐driven crossbar activity and ADC input range. For practical deployment, a low‐cost layer‐wise Bayesian search strategy optimizes perturbation parameters offline on a small subset, which are then fixed during inference. An 8 kb (64 × 128) 1T1R memristor‐array chip and hardware platform are developed for validation. On a hardware‐implemented MNIST convolutional neural network, the framework compresses the output current range by 2.02× while a limited accuracy drop from 95.4% to 93.8%. Additional evaluations demonstrate system energy reductions of 26.55% on ResNet‐18/CIFAR‐10 and 26.90% on VGG‐16/CIFAR‐100, with accuracy losses of 1.94% and 1.80%, respectively. On keyword spotting, selective perturbation compresses the output current range by 3.4× and reduces crossbar energy by 69.4%, while accuracy changes from 88.7% to 87.2%.