arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.
By Haibo HU, Lianming Huang, Qiao Li, Nan Guan, Chun Jason Xue
WARD is a runtime‑adaptive Vision Transformer framework designed for edge AI that combines channel‑wise subnetwork partitioning, reliability‑aware continual learning, and dynamic operating‑mode scheduling. It operates two physically isolated subnetworks across four modes—Full‑Precision, Low‑Power, High‑Reliability, and Adaptive—to balance computational cost and fault tolerance while maintaining uninterrupted inference. Implemented on a lightweight FPGA accelerator with minimal area overhead, WARD achieves a network‑level failure rate of 1.79% under high Bit Error Rates and supports rapid mode transitions within a few clock cycles.
By Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi, Ali Mahani
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
arXiv:2606. 31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers.
By Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, Jos\'e Cano
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak
arXiv:2609.23974v1 Announce Type: new
Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
By Boxun Hu, Jiawei Ge, Axel Krieger, Peng Wang, Tinoosh Mohsenin