Accurate and Compact Convolutional Neural Network Based on Stochastic Computing

Abstract Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many recognition problems. However, CNN models are computation-intensive and require enormous resources and power, limiting their applicability in embedded systems with limited area and power budget. An alternative computing technique called Stochastic Computing (SC) can implement resource-demanding algorithms in smaller hardware that indeed reduces the power consumption. In this work, we propose SC-based forward functions for CNN layers that obtain significant area savings and high accuracy to replace the conventional binary-encoded (BE) deterministic computing counterparts. Then, we specify some training considerations to enable achieving low error rates for SC-based CNN. The experimental results show that the SC-based CNN attained 99.19% and 96.25% classification accuracy using MNIST digit classification and AT&T face recognition datasets, respectively. Moreover, the SC-based CNN of ResNet-20 model achieved 86.5% classification accuracy using the CIFAR-10 object dataset. The SC-based CNN functions have better classification accuracy compared to other SC schemes and obtained ultra-low hardware footprint compared to conventional BE counterparts.