Efficient Optimized Testing of Resistive RAM Based Convolutional Neural Networks

Resistive random access memory (RRAM) based memristive crossbar arrays enable low power and low latency inference for convolutional neural networks (CNNs), making them suitable for deployment in IoT and edge devices. However RRAM cells within a crossbar suffer from conductance variations, making RRAM-based CNNs vulnerable to degradation of their classification accuracy. To address this, the classification accuracy of RRAM based CNN chips can be estimated using predictive tests, where a trained regressor predicts the accuracy of a CNN chip from the CNN’s response to a compact test dataset. In this research, we present a framework for co-optimizing the pixels of the compact test dataset and the regressor. The novelty of the proposed approach lies in the ability to co-optimize individual image pixels, overcoming barriers posed by the computational complexity of optimizing the large numbers of pixels in an image using state-of-the-art techniques. The co-optimization problem is solved using a three step process: a greedy image down selection followed by backpropagation driven image optimization and regressor fine-tuning. Experiments show that the proposed test approach reduces the CNN classification accuracy prediction error by $31 \%$ compared to the state of the art. It is seen that a compact test dataset with only 2-4 images is needed for testing, making the scheme suitable for built-in test applications.