Abstract: For decades, Moore’s Law and its partner Dennard Scaling have together enabled exponential computer systems performance improvements at manageable power dissipation. With the slowing of Moore/Dennard improvements, designers have turned to a range of approaches for extending scaling of computer systems performance and power efficiency. These include specialized accelerators and heterogeneous parallelism. Unfortunately, the scaling gains afforded by these techniques come with significant costs: increased hardware and software complexity, degraded programmability and portability, and increased likelihood of design errors and security vulnerabilities. The long-held hardware-software abstraction offered by the Instruction Set Architecture (ISA) interface is fading quickly in this post-ISA era. The talk will cover a range of design opportunities and challenges, with a particular emphasis on my group’s recent work on automated full-stack verification and optimization, and the surprising alignments between their applications in both classical and quantum computing systems.

Short Bio: Margaret Martonosi is the Hugh Trumbull Adams ’35 professor of computer science at Princeton University. She is also director of Princeton University’s Keller Center for Innovation in Engineering Education. Martonosi’s research interests are in computer architecture, where her prior work has included the development of the Wattch power modeling tool and the Princeton ZebraNet mobile sensor network project for the design and real-world deployment of zebra tracking collars in Kenya. Her current research focuses on hardware-software interface approaches to manage heterogeneous parallelism and system complexity in both classical and quantum computing architectures. Martonosi is a Fellow of both IEEE and ACM. Notable awards include the 2018 IEEE Technical Achievement Award, the 2010 Princeton University Graduate Mentoring Award, and the 2013 Anita Borg Institute Technical Leadership award. Her research has earned numerous best paper awards, as well as long-term impact awards from ACM SIGARCH, ACM SIGMOBILE, ACM SenSys, and IEEE HPCA.