We provide glimpses of our ongoing research. In the PHLOGON project, we are developing novel implementations of computing primitives. By employing self-sustaining oscillators that encode logic values in phase, PHLOGON offers superior noise immunity and, potentially, energy efficiency by exploiting emerging nano-technologies. Our Accurate Booleanization of Continuous Systems (ABCD) project is a new approach to the long-standing analog/mixed-signal verification problem. By replacing continuous-time blocks (eg, SPICE-level circuits) by finite state machine approximations generated automatically by algorithm, we are able to bring scalable techniques for purely Boolean verification to bear on AMS systems. Our Berkeley Eye Estimator (BEE) identifies worst-case eyes in modern communication links without optimism or pessimism. We have developed the first suite of models for memristive devices (including RRAM) that are well posed mathematically and simulate properly in circuits. Our MUSTARD algorithm enables device-level Random Telegraph Noise to be co-simulated in a statistically correct manner with circuits. Finally, we have developed the Model and Algorithm Prototyping Platform (MAPP), which removes a long-standing barrier to research in models/algorithms for continuous-time systems: the lack of a powerful yet convenient platform for prototyping new device models and simulation algorithms quickly.
Jaijeet Roychowdhury is a Professor in EECS at the University of California at Berkeley. He received a Bachelor's degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, India, in 1987, and a PhD degree in electrical engineering and computer science from UC Berkeley in 1993.