2021/01/23 by Xuecan Yang, Sumanta Chaudhuri, Yang, Xuecan +5
Computer Science · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning and ELM #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
paper · doi:10.48550/arxiv.2101.09492
openalex publication_date 2021/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Convolutional Neural Networks have achieved unprecedented success in image classification, recognition, or detection applications. However, their large-scale deployment in embedded devices is still limited by the huge computational requirements, i.e., millions of MAC operations per layer. In this article, MinConvNets where the multiplications in the forward propagation are approximated by minimum comparator operations are introduced. Hardware implementation of minimum operation is much simpler than multipliers. Firstly, a methodology to find approximate operations based on statistical correlation is presented. We show that it is possible to replace multipliers by minimum operations in the forward propagation under certain constraints, i.e. given similar mean and variances of the feature and the weight vectors. A modified training method which guarantees the above constraints is proposed. And it is shown that equivalent precision can be achieved during inference with MinConvNets by using transfer learning from well trained exact CNNs.