Although the convolutional neural network (CNN) has exhibited outstanding performance in various applications, the deployment of CNN on embedded and mobile devices
is limited by the massive computations and memory footprint. To address these challenges, Courbariaux and Bengio put forward binarized neural network (BNN) which
quantizes the weights and activations to ±1. From the perspective of hardware, BNN
can greatly simplify the computation and reduce the storage. In this work, we first
present the algorithm optimizations to further binarize the first layer and the padding
bits of BNN; then we propose a fully binarized CNN accelerator. With the ShuffleCompute structure and the memory-aware computation schedule scheme, the proposed
design can boost the performance for feature maps of different sizes and make full use
of the memory bandwidth. To evaluate our design, we implement the accelerator on
the Zynq ZC702 board, and the experiments on the SVHN and Cifar10 datasets show
state-of-the-art performance-efficiency and resource-efficiency