VLCQ: Post-training quantization for deep neural networks using variable length coding
[Widzenie komputerowe] Autorzy: Reem Abdel-Salam, Ahmed H. Abdel-Gawad, Amr G. Wassal
Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not require access to the original dataset or retraining the full model. Current methods that achieve high performance close to baseline results typically require INT8 fixed-point integers. However, achieving higher model compression with lower bit-widths often leads to significant performance degradation. In this paper, we propose VLCQ, a post-training quantization method that relaxes the fixed-point encoding constraint, enabling more flexible exploration of quantization techniques through variable-length encoding. This approach achieves competitive or improved performance while reducing bit-widths, without requiring access to training data or model fine-tuning. Extensive experiments on image classification, segmentation, and object detection …
- Kategoria arxiv
- cs.CV
- Kategoria
- Widzenie komputerowe
- Doi
- 10.1016/j.future.2024.107654