Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations

Yoonho Boo; Wonyong Sung

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arXiv · 1707.03684

Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations

Yoonho Boo·Wonyong Sung

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Abstract

Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference. Especially, fully-connected layers contain a large number of weights, thus they usually need many off-chip memory accesses for inference. We propose a weight compression method for deep neural networks, which allows values of +1 or -1 only at predetermined positions of the weights so that decoding using a table can be conducted easily. For example, the structured sparse (8,2) coding allows at most two non-zero values among eight weights. This method not only enables multiplication-free DNN implementations but also compresses the weight storage by up to x32 compared to floating-point networks. Weight distribution normalization and gradual pruning techniques are applied to mitigate the performance degradation. The experiments are conducted with fully-connected deep neural networks and convolutional neural networks.

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BibTeX RIS

Yoonho Boo, Wonyong Sung. 2017-07-01. Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations. https://arxiv.org/abs/1707.03684

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