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This paper introduces BinarEye: a digital processor for always-on BinaryConvolutional Neural Networks. The chip maximizes data reuse through a NeuronArray exploiting local weight Flip-Flops. It stores full network models andfeature maps and hence requires no off-chip bandwidth, which leads to a 2301b-TOPS/W peak efficiency. Its 3 levels of flexibility - (a) weightreconfiguration, (b) a programmable network depth and (c) a programmablenetwork width - allow trading energy for accuracy depending on the task'srequirements. BinarEye's full system input-to-label energy consumption rangesfrom 14.4uJ/f for 86% CIFAR-10 and 98% owner recognition down to 0.92uJ/f for94% face detection at up to 1700 frames per second. This is 3-12-70x moreefficient than the state-of-the-art at on-par accuracy.

BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS