Binary neural networks

Convolutional neural networks (CNNs) are used in a spread spectrum of machine learning applications, such as computer vision and speech recognition. Computation and memory accesses are the major challenges for the deployment of CNNs in resource -limited and low -power embedded systems. The recently proposed binary neural networks (BNNs) use just 1 bit for weights and/or activations instead of full precision values, hence substitute complex multiply -accumulation operations with bitwise logic operations to reduce the computation and memory footprint drastically. However, most BNN models come with some accuracy loss, especially in big datasets. Improving the accuracy of BNNs and designing efficient hardware accelerator for them are two important research directions that have attracted many attentions in recent years. In this chapter, we conduct a survey on the state-of-the-art researches on the design and hardware implementation of the BNN models.