Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks a cohesive, long-term vision to strategically coordinate the development of AI and HW. This fragmentation constrains progress toward holistic, sustainable, and adaptive AI systems capable of learning, reasoning, and operating efficiently across cloud, edge, and physical environments. The future of AI depends not only on scaling intelligence, but on scaling efficiency, achieving exponential gains in intelligence per joule, rather than unbounded compute consumption. Addressing this grand challenge requires rethinking the entire computing stack. This vision paper lays out a 10-year roadmap for AI+HW co-design and co-development, spanning algorithms, architectures, systems, and sustainability. We articulate key insights that redefine scaling around energy efficiency, system-level integration, and cross-layer optimization. We identify key challenges and opportunities, candidly assess potential obstacles and pitfalls, and propose integrated solutions grounded in algorithmic innovation, hardware advances, and software abstraction. Looking ahead, we define what success means in 10 years: achieving a 1000x improvement in efficiency for AI training and inference; enabling energy-aware, self-optimizing systems that seamlessly span cloud, edge, and physical AI; democratizing access to advanced AI infrastructure; and embedding human-centric principles into the design of intelligent systems. Finally, we outline concrete action items for academia, industry, government, and the broader community, calling for coordinated national initiatives, shared infrastructure, workforce development, cross-agency collaboration, and sustained public-private partnerships to ensure that AI+HW co-design becomes a unifying long-term mission.

翻译：人工智能（AI）与硬件（HW）正以前所未有的速度发展，然而它们的发展轨迹已变得密不可分。全球研究界缺乏一个连贯、长期的愿景来战略性地协调AI与HW的发展。这种碎片化状态制约了向整体性、可持续且自适应AI系统的进步，这些系统应能够在云端、边缘和物理环境中高效地学习、推理与运行。AI的未来不仅取决于智能的规模化，更取决于效率的规模化，即实现每焦耳智能的指数级提升，而非无限制的计算消耗。应对这一重大挑战需要重新思考整个计算栈。本愿景论文为AI+HW的协同设计与协同发展制定了一份为期十年的路线图，涵盖算法、架构、系统与可持续性。我们阐述了围绕能效、系统级集成和跨层优化重新定义规模化的关键见解。我们识别了关键挑战与机遇，坦率评估了潜在障碍与陷阱，并提出了基于算法创新、硬件进步和软件抽象的集成解决方案。展望未来，我们定义了十年后成功的含义：实现AI训练与推理效率提升1000倍；构建能够无缝跨越云端、边缘和物理AI的能源感知、自优化系统；普及先进AI基础设施的获取；以及将以人为本的原则嵌入智能系统的设计中。最后，我们为学术界、产业界、政府及更广泛的社群概述了具体行动项，呼吁协调的国家倡议、共享的基础设施、人才队伍建设、跨机构协作以及持续的公私合作伙伴关系，以确保AI+HW协同设计成为一项统一的长期使命。