Search Results for author:
Found 2 papers, 0 papers with code
Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction
To overcome these challenges, we propose a new post-training quantization method, which is the first to quantize efficient hybrid ViTs (MobileViTv1, MobileViTv2, Mobile-Former, EfficientFormerV1, EfficientFormerV2) with a significant margin (an average improvement of 8. 32\% for 8-bit and 26. 02\% for 6-bit) compared to existing PTQ methods (EasyQuant, FQ-ViT, and PTQ4ViT).
Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment
To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision representation is converted to a lower bit representation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
