DQA quantizes neural network activations to below 6 bits by using extra precision on important channels, truncating it with bit shifts, and Huffman-coding the truncation error, improving accuracy over direct quantization and NoisyQuant in most, but not all, tested settings.
ACM Transactions on Architecture and Code Optimization (TACO) (2024)
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DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations
DQA quantizes neural network activations to below 6 bits by using extra precision on important channels, truncating it with bit shifts, and Huffman-coding the truncation error, improving accuracy over direct quantization and NoisyQuant in most, but not all, tested settings.