  A low-power precision-scalable processor for ConvNets or convolutional neural
networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing
units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption
while maintaining throughput, this works is the first to both exploit the
sparsity of convolutions and to implement dynamic precision-scalability
enabling supply- and energy scaling. The processor is fully C-programmable,
consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W.
This system hereby outperforms the state-of-the-art up to 3.9x in energy
efficiency.
