State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution. Such networks strain the computational capabilities and energy available to embedded and mobile processing platforms, restricting their use in many important applications. In this paper, we push the boundaries of hardware-effective CNN design by proposing BCNN with Separable Filters (BCNNw/SF), which applies Singular Value Decomposition (SVD) on BCNN kernels to further reduce computational and storage complexity. To enable its implementation, we provide a closed form of the gradient over SVD to calculate the exact gradient with respect to every binarized weight in backward propagation. We verify BCNNw/SF on the MNIST, CIFAR-10, and SVHN datasets, and implement an accelerator for CIFAR-10 on FPGA hardware. Our BCNNw/SF accelerator realizes memory savings of 17% and execution time reduction of 31.3% compared to BCNN with only minor accuracy sacrifices.

通过在卷积神经网络内使用奇异值分解（SVD）来减少计算和存储复杂性的BCNNw / SF被提出，并且实现了对CIFAR-10数据集的FPGA硬件加速器，BCNNw / SF加速器相对于仅有BCNN可以节省17％的内存，并减少31.3％的执行时间，仅牺牲了轻微的准确性。

使用可分离滤波器的二值卷积神经网络，以实现高效硬件加速