# AI+HW 2035: Shaping the Next Decade

arXiv: https://arxiv.org/abs/2603.05225 · HTML: https://mlsanity.com/paper/2603.05225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 71 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 30 of 35)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks](https://mlsanity.com/paper/2412.15204/) (2024)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Data Poisoning Attacks on Factorization-Based Collaborative Filtering](https://mlsanity.com/paper/1608.08182/) (2016)
- [Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration](https://mlsanity.com/paper/1911.09925/) (2019)
- [ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks](https://mlsanity.com/paper/2502.05352/) (2025)
- [FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge](https://mlsanity.com/paper/1904.04421/) (2019)
- [EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions](https://mlsanity.com/paper/2005.02563/) (2020)
- [Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages](https://mlsanity.com/paper/2407.03321/) (2024)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems](https://mlsanity.com/paper/1909.09709/) (2019)
- [HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing](https://mlsanity.com/paper/2405.06067/) (2024)
- [A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators](https://mlsanity.com/paper/2105.12842/) (2021)
- [Intelligence per Watt: Measuring Intelligence Efficiency of Local AI](https://mlsanity.com/paper/2511.07885/) (2025)
- [TraCT: Disaggregated LLM Serving with CXL Shared Memory KV Cache at Rack-Scale](https://mlsanity.com/paper/2512.18194/) (2025)
- [DE-HNN: An effective neural model for Circuit Netlist representation](https://mlsanity.com/paper/2404.00477/) (2024)
- [Qplacer: Frequency-Aware Component Placement for Superconducting Quantum Computers](https://mlsanity.com/paper/2401.17450/) (2024)

## Built on by (most cited first, 1 of 1)

- [Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport](https://mlsanity.com/paper/2606.19562/) (2026)

## Abstract (arXiv)

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.