# FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

arXiv: https://arxiv.org/abs/2012.13113 · HTML: https://mlsanity.com/paper/2012.13113/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 23 of 23)

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- [F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization](https://mlsanity.com/paper/2202.05239/) (2022)
- [CPT: Efficient Deep Neural Network Training via Cyclic Precision](https://mlsanity.com/paper/2101.09868/) (2021)
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- [Auto-scaling Vision Transformers without Training](https://mlsanity.com/paper/2202.11921/) (2022)
- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation](https://mlsanity.com/paper/2112.11542/) (2021)
- [Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2505.01043/) (2025)
- [Dynamic Stashing Quantization for Efficient Transformer Training](https://mlsanity.com/paper/2303.05295/) (2023)
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- [Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving](https://mlsanity.com/paper/2110.03553/) (2021)
- [Accuracy Booster: Enabling 4-bit Fixed-point Arithmetic for DNN Training](https://mlsanity.com/paper/2211.10737/) (2022)
- [DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training](https://mlsanity.com/paper/2507.07149/) (2025)
- [InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks](https://mlsanity.com/paper/2104.10853/) (2021)
- [LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference](https://mlsanity.com/paper/2203.07713/) (2022)
- [A General and Efficient Training for Transformer via Token Expansion](https://mlsanity.com/paper/2404.00672/) (2024)
- [Better Schedules for Low Precision Training of Deep Neural Networks](https://mlsanity.com/paper/2403.02243/) (2024)
- [SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training](https://mlsanity.com/paper/2101.01163/) (2021)
- [CycleBNN: Cyclic Precision Training in Binary Neural Networks](https://mlsanity.com/paper/2410.00050/) (2024)
- [Auditing Invisible Weight Updates with Reference Traces](https://mlsanity.com/paper/2607.09800/) (2026)

## Abstract (arXiv)

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs. As reducing precision is one of the most effective knobs for boosting training time/energy efficiency, there has been a growing interest in low-precision DNN training. In this paper, we explore from an orthogonal direction: how to fractionally squeeze out more training cost savings from the most redundant bit level, progressively along the training trajectory and dynamically per input. Specifically, we propose FracTrain that integrates (i) progressive fractional quantization which gradually increases the precision of activations, weights, and gradients that will not reach the precision of SOTA static quantized DNN training until the final training stage, and (ii) dynamic fractional quantization which assigns precisions to both the activations and gradients of each layer in an input-adaptive manner, for only "fractionally" updating layer parameters. Extensive simulations and ablation studies (six models, four datasets, and three training settings including standard, adaptation, and fine-tuning) validate the effectiveness of FracTrain in reducing computational cost and hardware-quantified energy/latency of DNN training while achieving a comparable or better (-0.12%~+1.87%) accuracy. For example, when training ResNet-74 on CIFAR-10, FracTrain achieves 77.6% and 53.5% computational cost and training latency savings, respectively, compared with the best SOTA baseline, while achieving a comparable (-0.07%) accuracy. Our codes are available at: https://github.com/RICE-EIC/FracTrain.