Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing
We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training
ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing …
ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existing …
Q_YOLOv5m: A Quantization-based Approach for Accelerating Object Detection on Embedded Platforms
The deployment of deep learning models on resource-constrained embedded platforms
presents significant challenges due to limited computational power, memory, and energy …
presents significant challenges due to limited computational power, memory, and energy …
Towards Eight-Bit Quantization for 3D U-Net Medical Image Segmentation via ROI-Based Calibration and Background-Aware Shift
S Kim, M Cha, C Yi, XT Nguyen - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
3D U-Net medical image segmentation is essential for precise diagnosis and treatment
planning but comes with massive bandwidth requirements and considerable computational …
planning but comes with massive bandwidth requirements and considerable computational …
[PDF][PDF] Quantization Strategies in Federated Learning: Comparative Assessment of Methods and Challenges
P Dubey - Authorea Preprints, 2024 - techrxiv.org
FL presents an encouraging system for collectively training machine learning models on
decentralized devices while maintaining data privacy. However, the difficulties of restricted …
decentralized devices while maintaining data privacy. However, the difficulties of restricted …