FastKron: Efficient Quantization with Kronecker-Factored Hessians
We accelerate a family of algorithms for neural network quantization which utilize a two-sided version of the GPTQ/LDLQ algorithm. Standard GPTQ-style adaptive rounding uses one-sided correlation information derived from input activations. A natural two-sided extension can additionally capture correlations across output channels. It utilizes a general Kronecker-factored approximation of the weight matrix curvature. This approach has been used in BoA and YAQA. But making a concrete algorithmic implementation of this two-sided GPTQ variant is nontrivial. BoA uses a large number of sequential steps, while YAQA improves over the sequential depth but still has quartic total cost. We introduce ...
| Authors | Johann Birnick, Rayan Saab |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 27 September 2026 |
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