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.
Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration
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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.