Accurate and compact convolutional neural network based on stochastic computing

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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

CNNs excel in recognition tasks but are computationally intensive, while SC offers an alternative with lower power consumption and high accuracy; experimental results demonstrate that SC-based CNNs achieve high classification accuracy across various datasets.