MSQ computes and prunes least significant bits of weights directly from the full-precision parameters, cutting training memory and time for mixed-precision quantization compared with bit-splitting methods.
Post train- ing 4-bit quantization of convolutional networks for rapid- deployment
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MSQ: Memory-Efficient Bit Sparsification Quantization
MSQ computes and prunes least significant bits of weights directly from the full-precision parameters, cutting training memory and time for mixed-precision quantization compared with bit-splitting methods.