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In this paper, we propose a novel algorithm for compressing neural networks to reduce the memory requirements by using blocked hashing techniques. By adding blocked constraints on top of the conventional hashing technique, the test error rate is maintained while the spatial locality for the computations is preserved. Using this scheme, the synaptic connections are compressed by at least an order (10×)...
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