Dr. Arkaprava Basu is an Associate Professor at the Indian Institute of Science, where he mentors students in the Computer Systems Lab. Arka's research focuses on pushing the boundaries of memory management and software reliability for both CPUs and GPUs. His work spans diverse areas, from optimizing memory systems for chiplet-based GPUs to developing innovative techniques to eliminate synchronization bottlenecks in GPU programs. He is also a recipient of the Intel Rising Star Faculty Award, ACM India Early Career Award,  and multiple other accolades, recognizing his innovative approaches to enhancing GPU performance, programmability, and reliability.

Dr. Dan Sorin is a Professor of Electrical and Computer Engineering at Duke University, and a co-founder of Realtime Robotics. Dan is widely known for his pioneering work in memory systems. He has co-authored the seminal Primer on Memory Consistency and Cache Coherence, which has become a foundational resource for students and researchers alike. Dan’s contributions span from developing resilient systems that tolerate hardware faults to innovations in cache coherence protocols, and has been recognized by multiple best paper awards and patents. His work at Realtime Robotics has pushed the boundaries of autonomous motion planning, enabling real-time decision-making in dynamic environments.

Dr. Vijay Janapa Reddi is an Associate Professor at Harvard University, and Vice President and Co-founder of MLCommons. He has made substantial contributions to mobile and edge computing systems, and played a key role in developing the MLPerf Benchmarks. Vijay has authored the machine learning systems book mlsysbook.ai, as part of his twin passions of education and outreach. He received the IEEE TCCA Young Computer Architect Award in 2016, has been inducted in the MICRO and HPCA Halls of Fame, and is a recipient of multiple best paper awards. 

Dr. Carole-Jean Wu is a Director of AI Research at Meta. She is a founding member and a Vice President of MLCommons – a non-profit organization that aims to accelerate machine learning innovations for the benefits of all. Dr. Wu also serves on the MLCommons Board as a Director, chaired the MLPerf Recommendation Benchmark Advisory Board, and co-chaired for MLPerf Inference. Prior to Meta/Facebook, Dr. Wu was a professor at ASU. She earned her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University and a B.Sc. degree in Electrical and Computer Engineering from Cornell University.  Dr. Wu’s expertise sits at the intersection of computer architecture and machine learning. Her work spans across datacenter infrastructures and edge systems, such as developing energy- and memory-efficient systems and microarchitectures, optimizing systems for machine learning execution at-scale, and designing learning-based approaches for system design and optimization. She is passionate about pathfinding and tackling system challenges to enable efficient and responsible AI technologies.

Dr. Karu Sankaralingam is a Professor at the University of Wisconsin-Madison, an entrepeneur, inventor, as well as a Principal Research Scientist at NVIDIA.  His work has been featured in industry forums of Mentor and Synopsys, and has been covered by the New York Times, Wired, and IEEE Spectrum. He founded the hardware startup SimpleMachines in 2017 which developed chip designs applying dataflow computing to push the limits of AI generality in hardware and built the Mozart chip. In his career, he has led three chip projects: Mozart (16nm, HBM2 based design), MIAOW open source GPU on FPGA, and the TRIPS chip as a student during his PhD. In his research he has pioneered the principles of dataflow computing, focusing on the role of architecture, microarchitecture and the compiler. He has published over 100 research papers, has graduated 9 PhD students, is an inventor on 21 patents, and 9 award papers. He is a Fellow of IEEE.

Dr. Gabriel Loh is a Senior Fellow at AMD Research and Advanced Development. Gabe is known for his contributions to 3D die-stacked architectures, memory organization and caching techniques, and chiplet multicore architectures. His ideas have influenced multiple commercial products and industry standards. He is a recipient of ACM SIGARCH's Maurice Wilkes Award, is a Hall of Fame member for MICRO, HPCA, ISCA, and a recipient of the NSF CAREER award.

Dr. Vivienne Sze is an associate professor in the EECS department at MIT. Vivienne is recognized for her leading work on energy-efficient computing systems spanning a wide range of domains: from video compression, to machine learning, robotics and digital health. She received the DARPA Young Faculty Award, Edgerton Faculty Award, faculty grants from Google, Facebook and Qualcomm, and a Primetime Engineering Emmy as a member of the team that developed the High-Efficiency Video Coding standard.

This is a special episode to commemorate the 50th anniversary of SIGARCH. We have three leaders from our community who have served as SIGARCH chairs -- Dr. David Patterson, Dr. Norm Jouppi and Dr. Natalie Entright-Jerger -- reflect on the evolution of the computer architecture field as well as our community over half a century, and share their perspectives on opportunities and exciting times ahead.

David Patterson is a professor emeritus at ​​UC Berkeley, a distinguished engineer at Google, and recipient of the Turing Award. Norm Jouppi, a VP and Engineering Fellow at Google, where he is the chief architect for Google's Tensor Processing Units (TPUs), and a recipient of the Eckert-Mauchly award. Natalie Enright-Jerger is a professor at the University of Toronto, where she is the Canada Research Chair in Computer Architecture, and is a recipient of the Alfred P. Sloan Research Fellowship, and distinguished member of ACM and IEEE.

Jim Keller is the CTO of Tenstorrent, and a veteran computer architect. Prior to Tenstorrent, he has held roles of Senior Vice President at Intel, Vice President of Autopilot at Tesla, Vice President and Chief Architect at AMD, and at PA Semi which was acquired by Apple. Jim has led multiple successful silicon designs over the decades, from the DEC Alpha processors, to AMD K7/K8/K12, HyperTransport and the AMD Zen family, the Apple A4/A5 processors, and Telsa's self-driving car chip.

Dr. Brandon Lucia is a professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University. Prof. Lucia has made significant contributions to enabling capable and reliable intermittent computing systems, developing techniques that span the hardware-software stack from novel microarchitectures, to programming models and tools. He is a recipient of the IEEE TCCA Young Computer Architect Award, the Sloan Research Fellowship, and several best paper awards.