TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

• URL: http://arxiv.org/abs/2506.11431v1
• Date: Fri, 13 Jun 2025 03:08:18 GMT
• Title: TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision
• Authors: Jinhee Kim, Seoyeon Yoon, Taeho Lee, Joo Chan Lee, Kang Eun Jeon, Jong Hwan Ko,
• Abstract summary: Truncation is an effective approach for achieving lower bit precision mapping.<n>Current quantization-aware training schemes are not designed for the truncation process.<n>We propose TruncQuant, a novel truncation-ready training scheme allowing flexible bit precision through bit-shifting in runtime.
• Score: 8.532216260938478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deployment of deep neural networks on edge devices is a challenging task due to the increasing complexity of state-of-the-art models, requiring efforts to reduce model size and inference latency. Recent studies explore models operating at diverse quantization settings to find the optimal point that balances computational efficiency and accuracy. Truncation, an effective approach for achieving lower bit precision mapping, enables a single model to adapt to various hardware platforms with little to no cost. However, formulating a training scheme for deep neural networks to withstand the associated errors introduced by truncation remains a challenge, as the current quantization-aware training schemes are not designed for the truncation process. We propose TruncQuant, a novel truncation-ready training scheme allowing flexible bit precision through bit-shifting in runtime. We achieve this by aligning TruncQuant with the output of the truncation process, demonstrating strong robustness across bit-width settings, and offering an easily implementable training scheme within existing quantization-aware frameworks. Our code is released at https://github.com/a2jinhee/TruncQuant.

Related papers

• Low-bit Model Quantization for Deep Neural Networks: A Survey [123.89598730307208]
  This article surveys the recent five-year progress towards low-bit quantization on deep neural networks (DNNs)<n>We discuss and compare the state-of-the-art quantization methods and classify them into 8 main categories and 24 sub-categories according to their core techniques.<n>We shed light on the potential research opportunities in the field of model quantization.
  arXiv  Detail & Related papers  (2025-05-08T13:26:19Z)
• Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip [0.9187138676564589]
  We present High Granularity Quantization (HGQ), an innovative quantization-aware training method. HGQ fine-tune the per-weight and per-activation precision by making them optimizable through gradient descent. This approach enables ultra-low latency and low power neural networks on hardware capable of performing arithmetic operations.
  arXiv  Detail & Related papers  (2024-05-01T17:18:46Z)
• AdaQAT: Adaptive Bit-Width Quantization-Aware Training [0.873811641236639]
  Large-scale deep neural networks (DNNs) have achieved remarkable success in many application scenarios. Model quantization is a common approach to deal with deployment constraints, but searching for optimized bit-widths can be challenging. We present Adaptive Bit-Width Quantization Aware Training (AdaQAT), a learning-based method that automatically optimize bit-widths during training for more efficient inference.
  arXiv  Detail & Related papers  (2024-04-22T09:23:56Z)
• Vertical Layering of Quantized Neural Networks for Heterogeneous Inference [57.42762335081385]
  We study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. We can theoretically achieve any precision network for on-demand service while only needing to train and maintain one model.
  arXiv  Detail & Related papers  (2022-12-10T15:57:38Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment [15.720551497037176]
  We propose an auto-tuner known as Quantune to accelerate the search for the configurations of quantization. We show that Quantune reduces the search time for quantization by approximately 36.5x with an accuracy loss of 0.07 0.65% across six CNN models.
  arXiv  Detail & Related papers  (2022-02-10T14:05:02Z)
• Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss [61.26793005355441]
  Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks. DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons. We experimentally validate our method on various benchmark datasets and network architectures.
  arXiv  Detail & Related papers  (2021-09-05T15:15:07Z)
• A White Paper on Neural Network Quantization [20.542729144379223]
  We introduce state-of-the-art algorithms for mitigating the impact of quantization noise on the network's performance. We consider two main classes of algorithms: Post-Training Quantization (PTQ) and Quantization-Aware-Training (QAT)
  arXiv  Detail & Related papers  (2021-06-15T17:12:42Z)
• Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks [73.29587731448345]
  This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. Second, to obtain low bit-width activations, existing works consider all channels equally.
  arXiv  Detail & Related papers  (2020-12-26T15:21:18Z)
• DAQ: Distribution-Aware Quantization for Deep Image Super-Resolution Networks [49.191062785007006]
  Quantizing deep convolutional neural networks for image super-resolution substantially reduces their computational costs. Existing works either suffer from a severe performance drop in ultra-low precision of 4 or lower bit-widths, or require a heavy fine-tuning process to recover the performance. We propose a novel distribution-aware quantization scheme (DAQ) which facilitates accurate training-free quantization in ultra-low precision.
  arXiv  Detail & Related papers  (2020-12-21T10:19:42Z)
• Recurrence of Optimum for Training Weight and Activation Quantized Networks [4.103701929881022]
  Training deep learning models with low-precision weights and activations involves a demanding optimization task. We show how to overcome the nature of network quantization. We also show numerical evidence of the recurrence phenomenon of weight evolution in training quantized deep networks.
  arXiv  Detail & Related papers  (2020-12-10T09:14:43Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.