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.
Dory: Automatic end-to-end deployment of real-world dnns on low-cost iot mcus
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.