Work in progress. Architecture 2.0 is being built in the open and will keep changing. How this is written →
Papers, posts, talks, datasets, and workshop writeups that orient the field
This is the hub's curated starting point for Architecture 2.0 references. It is not meant to replace a bibliography; it points newcomers and submitters toward the papers, essays, talks, and artifacts that explain the motivation, evidence standards, and community infrastructure around AI-assisted computer architecture.
Paper · DAC 2023 / IEEE Xplore · 2023
Concise position paper framing the opportunities and community infrastructure needed for machine-learning-driven computer architecture.
Article · IEEE Computer · 2025
Foundations article arguing why AI agents and compound AI systems matter for modern computer system design.
Blog post · SIGARCH Computer Architecture Today · June 14, 2023
Community-facing argument for datasets, benchmarks, leaderboards, competitions, and open infrastructure for Architecture 2.0.
Blog post · SIGARCH Computer Architecture Today · December 20, 2023
Writeup from the online community workshop, summarizing workstreams around datasets, algorithms, tools, best practices, workforce, and industry.
Blog post · SIGARCH Computer Architecture Today · February 4, 2025
A systems-oriented essay on abstractions for intelligent and compound AI systems.
Blog post · SIGARCH Computer Architecture Today · May 19, 2026
Recent SIGARCH essay connecting agentic co-design to hardware-software contracts, memory systems, and datacenter architecture.
Paper · ISCA 2023 · 2023
Open-source framework that connects search algorithms to architecture simulators for fairer, repeatable ML-assisted design-space exploration.
Paper · IEEE Computer Architecture Letters · 2025
Question-answering dataset for evaluating and improving language-model reasoning about computer architecture.
Podcast · Computer Architecture Podcast · September 3, 2024
Podcast conversation introducing Architecture 2.0 and AI for computer systems design to a broader architecture audience.
Paper · ISSCC 2020 keynote companion · 2020
Jeff Dean's broad framing of how deep learning changes hardware demand and how machine learning can begin to influence circuit and chip design.
Talk · DAC 2021 keynote · December 6, 2021
A keynote on using machine learning across hardware-design tasks, useful background for the Architecture 2.0 design-loop agenda.
Talk · NSF AI for EDA Workshop at NeurIPS 2024 · December 2024
A forward-looking talk on automating chip design across architecture choices, logic design, verification, and floorplanning.
Paper · Nature · 2021
Influential reinforcement-learning approach to chip macro placement and a concrete example of AI entering a real hardware-design loop.
Blog post · Google DeepMind · September 2024
Accessible update on AlphaChip's chip-design impact, model release, and use in Google accelerator design.
Paper · arXiv · 2021
A broad survey of machine learning as a predictive-modeling and design methodology for computer architecture and systems.
Blog post · CRA Industry · April 27, 2026
Community visioning notes on AI, specialization, hardware-design automation, and agent-managed software and systems development.
Report · NSF AI for EDA Workshop / arXiv · 2026
Workshop report summarizing AI-for-EDA needs around collaboration, foundation methods, data infrastructure, compute, verification, and workforce development.