The development of computing systems able to address our ever-increasing needs, especially as we reach the end of CMOS transistor scaling, requires truly novel methods of computing. My research draws inspiration from biology, rethinks the digital/analog boundary, and challenges conventional wisdom, which typically guides how we perform computation, by reimagining the role of time. In this talk, I first introduce a computational temporal logic that sets the foundation for temporal computing. Second, I demonstrate how this foundation opens up unique ways in which we can work with sensors and design machine learning systems. Third, I describe how temporal operators provide answers to several long-lasting problems in computing with emerging devices. Finally, I touch upon future work with themes ranging from in-sensor online learning to hybrid quantum-classical computing and formally verifiable hardware.
Bio: George Tzimpragos is a Ph.D. candidate in Computer Science at UC Santa Barbara and a research affiliate at Lawrence Berkeley National Laboratory. His research explores how computer architecture concepts, along with a deeper understanding of the nature of computation and devices, can be used to develop new paradigms and cross-stack solutions for emerging applications and technologies. His work has been published in top conferences and journals and rewarded with an ASPLOS best paper award, an IEEE Micro top pick, a CACM research highlight, and an invited oral presentation at EUCAS. He is also the creator of https://thejjunction.org, a platform for the exchange of ideas and resources on superconducting computing. His personal website is https://georgetzimpragos.com.