The paper introduces BEVPIPE, a framework that enables deployment of bird's-eye-view (BEV) perception models—used in autonomous driving to fuse camera and LiDAR data—on portable GPU compute APIs. By partitioning the model into dense subgraphs managed by standard inference runtimes and handling sparse 3D convolutions and geometric scatter operations through external extensions, BEVPIPE achieves a 19.5× speedup while preserving 98.5% of the reference mean average precision. The approach also demonstrates portability across different GPU backends, breaking the current reliance on CUDA-only, PyTorch‑coupled sparse convolution libraries.
By Rohit Verma, Anand V Bodas
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
arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
By Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha, Khoa Vo, Philip Lund M{\o}ller, Quang T. Nguyen, Long Dinh, Tuan Dam, Vu Duong, Tung M. Luu, Trung Le, Tran Nguyen Le, Minh Vu, An Thai Le, Ngan Le, Daniel Sonntag, James Zou, Jan Peters, Duy M. H. Nguyen, Ngo Anh Vien
arXiv:2506. 20686v2 Announce Type: replace-cross Abstract: Recent advances in biomolecular modeling have been catalyzed by models such as AlphaFold3 (AF3), which introduce science-informed changes to the transformer architecture.
By Hoa La, Ahan Gupta, Alex Morehead, Jianlin Cheng, Minjia Zhang
Robion is a new serving and management system designed to run Vision‑Language‑Action (VLA) models on multi‑GPU edge servers for robot factories. It splits the VLM and ADiT stages within a single GPU, shares streams across multiple models, and prioritizes requests by remaining SLO time, enabling high robot load while meeting strict latency requirements. In experiments, Robion achieves 6.7× higher robot load than vLLM‑Omni and 1.5× higher than a monolithic pipeline, and can serve 64 robots on a 4‑GPU server with 98% SLO attainment.
By Dionysios Adamopoulos, Nattapol Chanpaisit, Basel Fakhri, Christina Giannoula
arXiv:2607. 12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks.
By Zebin Yang, Qi Wang, Yunhe Wang, Xiurui Guo, Bo Yu, Shaoshan Liu, Jiafeng Xu, Hao Dong, Meng Li