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A precision-scalable processor for low-power ConvNets or convolutional neural networks is implemented in a 40-nm CMOS technology. To minimize energy consumption while maintaining throughput, this paper is the first to implement dynamic precision and energy scaling and exploit the sparsity of convolutions in a dedicated processor architecture. The processor’s 256 parallel processing units achieve a...
ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification algorithms, achieving near-human performance in visual recognition [1]. New trends such as augmented reality demand always-on visual processing in wearable devices. Yet, advanced ConvNets achieving high recognition rates are too expensive in terms of energy as they require substantial data movement and billions of...
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