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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Accès en ligne: | https://arxiv.org/abs/2603.05225 |
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| _version_ | 1866917316147544064 |
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| author | Chen, Deming Cong, Jason Mirhoseini, Azalia Kozyrakis, Christos Mitra, Subhasish Xiong, Jinjun Young, Cliff Anandkumar, Anima Littman, Michael Kirschen, Aron Shao, Sophia Leef, Serge Shanbhag, Naresh Milojicic, Dejan Schulte, Michael Cauwenberghs, Gert Chow, Jerry M. Dao, Tri Gopalakrishnan, Kailash Ho, Richard Kim, Hoshik Olukotun, Kunle Pan, David Z. Ren, Mark Roth, Dan Singh, Aarti Sun, Yizhou Wang, Yusu LeCun, Yann Puri, Ruchir |
| author_facet | Chen, Deming Cong, Jason Mirhoseini, Azalia Kozyrakis, Christos Mitra, Subhasish Xiong, Jinjun Young, Cliff Anandkumar, Anima Littman, Michael Kirschen, Aron Shao, Sophia Leef, Serge Shanbhag, Naresh Milojicic, Dejan Schulte, Michael Cauwenberghs, Gert Chow, Jerry M. Dao, Tri Gopalakrishnan, Kailash Ho, Richard Kim, Hoshik Olukotun, Kunle Pan, David Z. Ren, Mark Roth, Dan Singh, Aarti Sun, Yizhou Wang, Yusu LeCun, Yann Puri, Ruchir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05225 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI+HW 2035: Shaping the Next Decade Chen, Deming Cong, Jason Mirhoseini, Azalia Kozyrakis, Christos Mitra, Subhasish Xiong, Jinjun Young, Cliff Anandkumar, Anima Littman, Michael Kirschen, Aron Shao, Sophia Leef, Serge Shanbhag, Naresh Milojicic, Dejan Schulte, Michael Cauwenberghs, Gert Chow, Jerry M. Dao, Tri Gopalakrishnan, Kailash Ho, Richard Kim, Hoshik Olukotun, Kunle Pan, David Z. Ren, Mark Roth, Dan Singh, Aarti Sun, Yizhou Wang, Yusu LeCun, Yann Puri, Ruchir Artificial Intelligence Hardware Architecture 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. |
| title | AI+HW 2035: Shaping the Next Decade |
| topic | Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2603.05225 |