Approximate On-chip Memory Optimization Method For Deep Residual Networks

Approximate memory is a significant improvement in a variety of application domains, especially which are both computationally intensive and memory intensive just as image classification. In this paper, we proposed an approximate on-chip memory optimization method with bit-width scaling and random bit flipping suited the deep residual neural network, which can be used to take the image classification. We evaluate the power and area benefits of our method. It demonstrated about 66.51% improvement in memory power and 56.93% improvement in memory areas with 0.91% quality loss for ResNet-50 across ImageNet. We also use this method for ResNet-101 and ResNet152, and achieve similar results.