Masked vector quantization (MVQ) prunes unimportant weights before clustering and uses masked k-means to build codebooks, improving accuracy over conventional VQ while cutting FLOPs and enabling a smaller, more efficient accelerator.
Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
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MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization
Masked vector quantization (MVQ) prunes unimportant weights before clustering and uses masked k-means to build codebooks, improving accuracy over conventional VQ while cutting FLOPs and enabling a smaller, more efficient accelerator.