查看文章

This paper introduces BinarEye: the first digital processor for always-on Binary Convolutional Neural Networks. The chip maximizes data reuse through a Neuron Array exploiting local weight Flip-Flops. It stores full network models and feature maps and hence requires no off-chip bandwidth, which leads to a 230 lb-TOPS/W peak efficiency. Its 3-levels of flexibility - (a) weight reconfiguration, (b) a programmable network depth and (c) a programmable network width - allow trading energy for accuracy depending on the task's requirements. BinarEye's full-system input-to-label energy consumption ranges from 14.4uJ/f for 86%/CIFAR-10 and 98%/owner recognition down to 0.92uJ/f for 94%/face detection at up to 1700 frames per second. This is 3-12-70× more efficient than the state-of-the-art at on par accuracy.