Readings

Concise position paper framing the opportunities and community infrastructure needed for machine-learning-driven computer architecture.

Authors

Vijay Janapa Reddi; Amir Yazdanbakhsh

DOI

10.1109/DAC56929.2023.10247808

Foundations article arguing why AI agents and compound AI systems matter for modern computer system design.

Authors

Vijay Janapa Reddi; Amir Yazdanbakhsh

DOI

10.1109/MC.2024.3521641

Community-facing argument for datasets, benchmarks, leaderboards, competitions, and open infrastructure for Architecture 2.0.

Authors

Vijay Janapa Reddi; Amir Yazdanbakhsh

Writeup from the online community workshop, summarizing workstreams around datasets, algorithms, tools, best practices, workforce, and industry.

Authors

Siddharth Garg et al.

A systems-oriented essay on abstractions for intelligent and compound AI systems.

Authors

Amir Yazdanbakhsh

Recent SIGARCH essay connecting agentic co-design to hardware-software contracts, memory systems, and datacenter architecture.

Authors

Dimitrios Skarlatos

Open-source framework that connects search algorithms to architecture simulators for fairer, repeatable ML-assisted design-space exploration.

Authors

Srivatsan Krishnan et al.

DOI

10.1145/3579371.3589049

Question-answering dataset for evaluating and improving language-model reasoning about computer architecture.

Authors

Shvetank Prakash et al.

DOI

10.1109/LCA.2025.3541961

Podcast conversation introducing Architecture 2.0 and AI for computer systems design to a broader architecture audience.

Authors

Suvinay Subramanian; Lisa Hsu; Vijay Janapa Reddi

Jeff Dean's broad framing of how deep learning changes hardware demand and how machine learning can begin to influence circuit and chip design.

Authors

Jeffrey Dean

DOI

10.1109/ISSCC19947.2020.9063049

A keynote on using machine learning across hardware-design tasks, useful background for the Architecture 2.0 design-loop agenda.

Authors

Jeffrey Dean

A forward-looking talk on automating chip design across architecture choices, logic design, verification, and floorplanning.

Authors

Jeffrey Dean

Influential reinforcement-learning approach to chip macro placement and a concrete example of AI entering a real hardware-design loop.

Authors

Azalia Mirhoseini; Anna Goldie; Mustafa Yazgan; Joe Wenjie Jiang; et al.

DOI

10.1038/s41586-021-03544-w

Accessible update on AlphaChip's chip-design impact, model release, and use in Google accelerator design.

Authors

Anna Goldie; Azalia Mirhoseini

A broad survey of machine learning as a predictive-modeling and design methodology for computer architecture and systems.

Authors

Nan Wu; Yuan Xie

Community visioning notes on AI, specialization, hardware-design automation, and agent-managed software and systems development.

Workshop report summarizing AI-for-EDA needs around collaboration, foundation methods, data infrastructure, compute, verification, and workforce development.

Authors

Deming Chen; Vijay Ganesh; Weikai Li; Yingyan Lin; Yong Liu; Subhasish Mitra; David Z. Pan; Ruchir Puri; Jason Cong; Yizhou Sun