AI+HW 2035: Shaping the Next Decade
🤖 AI Summary
This work addresses the lack of a long-term co-design strategy between artificial intelligence and hardware development, which currently limits system energy efficiency, sustainability, and cross-environment adaptability. To overcome these challenges, the paper proposes a ten-year co-design roadmap targeting 2035 that shifts the AI scaling paradigm from raw computational scale to energy efficiency, aiming for a thousand-fold improvement in energy-performance metrics. The approach integrates algorithmic, architectural, systems-level, and sustainability considerations into a unified optimization framework, bridging cloud, edge, end devices, and the physical world to realize self-optimizing intelligent systems. Key enablers include cross-layer co-design, energy-aware algorithms, novel hardware architectures, and efficient software abstractions, collectively advancing a human-centric, energy-efficient next-generation AI infrastructure.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
AI-HW co-design
energy efficiency
sustainable AI
computing stack
intelligent systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-HW co-design
energy efficiency
cross-layer optimization
intelligent systems
sustainable computing
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