HyQ: Hardware-Friendly Post-Training Quantization for CNN-Transformer Hybrid Networks
Authors: Nam Joon Kim, Jongho Lee, Hyun Kim
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed quantization method with INT8 precision demonstrated a 0.39% accuracy drop compared with the FP32 baseline on Mobile Vi T-s with the Image Net-1k dataset. |
| Researcher Affiliation | Collaboration | Nam Joon Kim1 , Jongho Lee1,2 and Hyun Kim1 1Department of Electrical and Information Engineering and Research Center for Electrical and Information Technology, Seoul National University of Science and Technology 2Squeeze Bits Inc. |
| Pseudocode | Yes | Algorithm 1 Integer-only Linear Exponential |
| Open Source Code | Yes | The code is available at https://github.com/IDSL-Seoul Tech/Hy Q. |
| Open Datasets | Yes | The proposed Hy Q framework was validated using the Image Net-1k benchmark dataset [Deng et al., 2009]. |
| Dataset Splits | Yes | The proposed Hy Q framework was validated using the Image Net-1k benchmark dataset [Deng et al., 2009]. To optimize the QADS parameters, we used 100 unlabeled images from the Image Net-1k training set. |
| Hardware Specification | Yes | Notably, applying QADS to Mobile Vi T-xxs took only 2.5 minutes on a single NVIDIA Ge Force RTX 3090 GPU. [...] We compared the synthesis results of two approaches [...] designed in RTL using the XC7Z0101 chipset in the Zynq-7000 board at 125 MHz. |
| Software Dependencies | No | We used the Py Torch framework [Paszke et al., 2019] for all experiments and quantized the pre-trained models provided by the Py Torch Image Models library [Wightman, 2019]. |
| Experiment Setup | Yes | We performed 1,000 iterations with a batch size of 100 using the Adam optimizer [Kingma and Ba, 2014]. |