Papers › A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez
Fully quantized training (FQT), which uses low-bitwidth hardware by quantizing the activations, weights, and gradients of a neural network model, is a promising approach to accelerate the training of deep neural networks. One major challenge with FQT is the lack of theoretical understanding, in particular of how gradient quantization impacts convergence properties. In this paper, we address this problem by presenting a statistical framework for analyzing FQT algorithms. We view the quantized gradient of FQT as a stochastic estimator of its full precision counterpart, a procedure known as quantization-aware training (QAT). We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance. Inspired by these theoretical results, we develop two novel gradient quantizers, and we show that these have smaller variance than the existing per-tensor quantizer. For training ResNet-50 on ImageNet, our 5-bit block Householder quantizer achieves only 0.5% validation accuracy loss relative to QAT, comparable to the existing INT8 baseline.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Linguistic Acceptability | CoLA | PSQ (Chen et al., 2020) | Accuracy | 67.5 | #21 of 43 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | PSQ (Chen et al., 2020) | Matched | 89.9 | #13 of 67 | Archive leaderboard | report |
| Natural Language Inference | QNLI | PSQ (Chen et al., 2020) | Accuracy | 94.5 | #15 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | PSQ (Chen et al., 2020) | Accuracy | 86.8 | #23 of 90 | Archive leaderboard | report |
| Semantic Textual Similarity | MRPC | PSQ (Chen et al., 2020) | Accuracy | 90.4 | #13 of 45 | Archive leaderboard | report |
| Semantic Textual Similarity | STS Benchmark | PSQ (Chen et al., 2020) | Pearson Correlation | 0.919 | #10 of 66 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | PSQ (Chen et al., 2020) | Accuracy | 96.2 | #20 of 87 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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