FBNA: A Fully Binarized Neural Network Accelerator
Peng Wei Guo, Hong Ma, Ruizhi Chen, Pin Li, Shaolin Xie, Donglin Wang · 2018
In recent researches, binarized neural network (BNN) has been proposed to address the massive computations and large memory footprint problem of the convolutional neural network (CNN). Several works have designed specific BNN accelerators and showed very promising results. Nevertheless, only part of the neural network is binarized in their architecture and the benefits of binary operations were not fully exploited. In this work, we propose the first fully binarized convolutional neural network accelerator (FBNA) architecture, in which all convolutional operations are binarized and unified, even including the first layer and padding. The fully unified architecture provides more resource, parallelism and scalability optimization opportunities. Compared with the state-of-the-art BNN accelerator, our evaluation results show 3.1x performance, 5.4x resource efficiency and 4.9x power efficiency on CIFAR-10.