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Deep neural networks have been achieving booming breakthroughs in various artificial intelligence tasks, however they are notorious for consuming unbearable hardware resources, training time and power. The emerging pruning/binarization methods, which aim at both decreasing overheads and retaining high performance, seem to promise applications on portable devices. However, even with these most advanced...
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