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.