AI+HW 2035: Shaping the Next Decade

• URL: http://arxiv.org/abs/2603.05225v1
• Date: Thu, 05 Mar 2026 14:36:33 GMT
• Title: AI+HW 2035: Shaping the Next Decade
• Authors: Deming Chen, Jason Cong, Azalia Mirhoseini, Christos Kozyrakis, Subhasish Mitra, Jinjun Xiong, Cliff Young, Anima Anandkumar, Michael Littman, Aron Kirschen, Sophia Shao, Serge Leef, Naresh Shanbhag, Dejan Milojicic, Michael Schulte, Gert Cauwenberghs, Jerry M. Chow, Tri Dao, Kailash Gopalakrishnan, Richard Ho, Hoshik Kim, Kunle Olukotun, David Z. Pan, Mark Ren, Dan Roth, Aarti Singh, Yizhou Sun, Yusu Wang, Yann LeCun, Ruchir Puri,
• Abstract summary: Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined.<n>This vision paper lays out a 10-year roadmap for AI+HW co-design and co-development, spanning algorithms, architectures, systems, and sustainability.<n>We identify key challenges and opportunities, candidly assess potential obstacles and pitfalls, and propose integrated solutions.
• Score: 135.53570243498987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI [2.134211474877041]
  Recent evidence shows that generative AI disproportionately affects highly educated, white collar work.<n>This paper proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy.<n>The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work.
  arXiv  Detail & Related papers  (2026-01-10T09:27:56Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• From Autonomous Agents to Integrated Systems, A New Paradigm: Orchestrated Distributed Intelligence [0.0]
  We introduce the concept of Orchestrated Distributed Intelligence (ODI)<n>ODI reconceptualizes AI as cohesive, orchestrated networks that work in tandem with human expertise.<n>Our work outlines key theoretical implications and presents a practical roadmap for future research and enterprise innovation.
  arXiv  Detail & Related papers  (2025-03-17T22:21:25Z)
• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
  This paper presents a comprehensive overview of AI and communication for 6G networks.<n>We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.<n>The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
  arXiv  Detail & Related papers  (2024-12-19T05:36:34Z)
• Transforming the Hybrid Cloud for Emerging AI Workloads [82.21522417363666]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.<n>The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.<n>This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• AI in Action: Accelerating Progress Towards the Sustainable Development Goals [4.09375125119842]
  We draw on Google's internal and collaborative research, technical work, and social impact initiatives to show AI's potential to accelerate action on the UN's Sustainable Development Goals. The paper highlights AI capabilities (including computer vision, generative AI, natural language processing, and multimodal AI) and showcases how AI is altering how we approach problem-solving across all 17 SDGs. We then offer insights on AI development and deployment to drive bold and responsible innovation, enhance impact, close the accessibility gap, and ensure that everyone, everywhere, can benefit from AI.
  arXiv  Detail & Related papers  (2024-07-02T23:25:27Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• Future Computer Systems and Networking Research in the Netherlands: A Manifesto [137.47124933818066]
  We draw attention to CompSys as a vital part of ICT. Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
  arXiv  Detail & Related papers  (2022-05-26T11:02:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.