AQT is a quantization library designed to allow utilization of low-bit and high-performance numerics of contemporary ML hardware accelerators. AQT supports both research and production1, but focuses on the latter.
Please use a following bibtex entry:
@software{aqt2022github,
author = {Lew, Lukasz and Feinberg, Vlad and Agrawal, Shivani and Lee, Jihwan and Malmaud, Jonathan and Wang, Lisa and Dormiani, Pouya and Pope, Reiner },
title = {AQT: Accurate Quantized Training)},
url = {http://github.com/google/aqt},
year = {2022},
}
Footnotes
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The support for research is exemplified by having a state of the art quantization quality on standard models such as ResNet and Transformer. The production aspect is defined as high performance and robust out-of-the-box working results with good defaults. ↩