arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
By Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hassan Najafi
The paper introduces RAMP, a method for robust adaptive mixed‑precision quantization of vision models on edge CPUs. It evaluates 13 sensitivity metrics across four neural networks, finding that Jensen‑Shannon Divergence consistently identifies layers that can be safely quantized. Using K‑Means clustering on these metrics, RAMP achieves near‑lossless accuracy with an average 1.81× speed‑up, while cautioning against excluding low‑speed‑up layers that can fragment the computational graph.
By David Poblaci\'on-Criado, Dario Garcia-Gasulla, Eduardo Quinones
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. Howeve...
arXiv:2506.11784v2 Announce Type: replace
Abstract: Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-wid...
By Guang Liang, Xinyao Liu, Jianxin Wu
arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.
By Vaishnav Raju
SCULPT is a training-time method that enhances the readiness of edge vision models for low-bit post‑training quantization (PTQ). It introduces a topology‑aware activation regularizer to reduce skewness and kurtosis, and a stable percentile‑based clipping mechanism that learns deployment‑ready activation bounds during ordinary FP32 fine‑tuning. The resulting clipping bounds can be directly exported into standard PTQ workflows for INT8 or lower‑bit settings such as W4A8.
By Bharadwaj Kavuri, Sourav Babu-PK, Varadhraj Ellapan, Pullarao Maddu, Prasad Deshpande