Acknowledgments
This book grew out of work and conversations across computer architecture, machine learning systems and benchmarking. I am grateful to the students, collaborators, colleagues, and broader research community who pressure-tested the framing, challenged weak claims, and insisted on evidence over enthusiasm. Their questions and examples shaped the emphasis on design loops, evidence standards, rejection, and human architectural judgment throughout the book.
I am especially grateful to Partha Ranganathan, Vice President and Engineering Fellow at Google, who delivered the inaugural keynote for the online Architecture 2.0 workshop.
For feedback, discussions, and challenges that sharpened this work, I am also grateful to Siddharth Garg (New York University), Brian Hirano (Micron), Jenny Huang (NVIDIA), Tushar Krishna (Georgia Institute of Technology), Srivatsan Krishnan (NVIDIA), Benjamin Lee (University of Pennsylvania), Yingyan (Celine) Lin (Georgia Institute of Technology), Jason Lowe-Power (University of California, Davis), Martin Maas (Google DeepMind), Ankita Nayak (Qualcomm), Matt Sinclair (University of Wisconsin-Madison), Srinivas Sridharan (NVIDIA), Zishen Wan (Columbia University), Amir Yazdanbakhsh (Google DeepMind), and Cliff Young (Google DeepMind).
Any errors that remain are my own.