It is argued that progress in vision generative AI has been driven by output quality, with hardware evolving reactively to accommodate growing model demands, making generative AI deployment sustainable and accessible across a much broader range of platforms.
Abstract
Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning. The explosion of multimodal models has made them widely associated with text-to-image applications running on large datacentres. However, vision generative models are equally needed in applications that operate under strict hardware constraints at the edge, including autonomous vehicles, agricultural sensors, and mobile devices. In this Perspective, we argue that progress in vision generative AI has been driven by output quality, with hardware evolving reactively to accommodate growing model demands. We quantify the parameter cost and energy efficiency of these models across a range of accelerator platforms, and map four generative model families against seven real-world application domains. Finally, we advocate a software-hardware co-design approach, where deployment constraints are considered from the start of the design process, ensuring that the"right model"runs on the"right hardware"to serve the"right application", making generative AI deployment sustainable and accessible across a much broader range of platforms.
This survey provides a comprehensive overview of techniques that enable GenAI deployment at the edge, covering software optimizations, hardware innovations, and system-level frameworks, with particular emphasis on hardware-focused approaches.
Mozhgan Navardi, Yuzhe Fu, Yueqian Lin et al.· ACM Computing Surveys· 0 citations
Physical AI -- the integration of large vision-language-action (VLA) models with embodied agents that act in the real world -- has emerged as the next major frontier for AI, echoed by industry leaders such as Jensen Huang (``the next big thing is Physical AI, AI with a body,''GTC Paris, June 2025) and Dr. Lisa Su (`we're entering the world of Physical AI ... this is where AI enters the real world,'CES 2026). This paper presents an end-to-end, fully AMD-accelerated technology stack for embodied manipulation, spanning data-center training silicon, Radeon PRO simulation/rendering GPUs, and Ryzen AI edge compute, unified by the open ROCm software stack. We demonstrate that training and deploying VLA-based manipulation policies does not require a CUDA-locked ecosystem. Four progressive demonstrations are presented: (1) a Sim-to-Real manipulation pipeline trained with SmolVLA and deployed on a physical Franka arm; (2) a semantic, language-grounded object-selection task (`one-of-three'); (3) a Real2Sim synthetic-data generation pipeline that fuses 3D Gaussian Splatting (3DGS) reconstructions of real scenes with the Genesis physics engine; and (4) large-scale reinforcement learning for quadruped and humanoid locomotion benchmarked across multiple hardware platforms. All pipelines run natively on ROCm + PyTorch on RDNA4 (Radeon AI PRO R9700) and RDNA3.5 (Radeon PRO W7900) hardware and are reproducible on the free Radeon Cloud Platform.
Qingwen Yang, Xunyue Wang, Ziguan Wang et al.· 0 citations
MoWorld is the first real-time interactive World Model built on the Neural Processing Unit (NPU) and can achieves up to 50 FPS in such the devices, enabling practical and efficient deployment at scale.
Team Moxin, Deyi Ji, Tianrun Chen et al.· arXiv.org· 1 citation
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering’s grandest challenges: achieving reliable, fully autonomous driving, particularly the pursuit of Level 5 autonomy. This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack. We delve into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making. We categorize practical applications, such as end-to-end driving strategies and closed-loop simulations. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation, safety, and onboard deployment. By unifying these threads, the survey provides a forward-looking reference for researchers, engineers, and policymakers navigating the convergence of generative AI and advanced autonomous mobility. An actively maintained repository of cited works is available at https://github.com/taco-group/GenAI4AD.
Yuping Wang, Shuo Xing, Cui Can et al.· ACM Computing Surveys· 56 citations· ⚡2
Generative communications (GenCom), a novel paradigm for 6G networks in which large AI models drive semantic understanding, reasoning, and content generation, embedding these into the communication process, is introduced.
Extensive experiments demonstrate that the on-device latency-informed design combined with the tailored training strategy establishes a new state-of-the-art for efficient LVLM encoding, significantly outperforming existing encoder-centric baselines while operating on-device at nearly 1.7xthe speed.
Ioannis Maniadis Metaxas, Adrian Bulat, Alberto Baldrati et al.· arXiv.org· 0 citations
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