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Deep convolutional neural networks have shown outstanding performance in several speech and image recognition tasks. However they demand high computational complexity which limits their deployment in resource limited machines. The proposed work lowers the hardware complexity by constraining the learned convolutional kernels to be separable and also reducing the word-length of these kernels and other...
Deep convolutional neural networks have shown promising results in image and speech recognition applications. The learning capability of the network improves with increasing depth and size of each layer. However this capability comes at the cost of increased computational complexity. Thus reduction in hardware complexity and faster classification are highly desired. This work proposes an optimization...
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