WAPS-Quant: Low-Bit Post-Training Quantization Using Weight-Activation Product Scaling
Post-Training Quantization (PTQ) has been effectively compressing neural networks into very few bits using a limited calibration dataset. Various quantization methods utilizing second-order error have been proposed and demonstrated good performance. However, at extremely low bits, the increase in qu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10982219/ |
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