FET: Small: Increasing Robustness, Efficacy, and Capability of CMOS-Compatible Electronic Ising Machines

University Of Rochester, Rochester NY

Investigators

Abstract

Optimization problems are prevalent in modern societies. Solving them quickly and efficiently is a key enabler for continued economic growth and sustainability. Physics-inspired algorithms have long helped scientists and engineers solve these problems. More recently, researchers have gone one step further and used physical processes to directly perform “physical computation”. When researchers can finally bring such a technology to the marketplace, it is anticipated that for some optimization problems, specialized hardware can improve the speed and energy efficiency by thousands or even millions of times. The project will allow the researchers to provide excellent opportunities for both graduate and under-graduate students to participate in cutting-edge inter-disciplinary research and become better prepared to contribute to the future workforce. Based on the foundation of prior work leading to this project, the goal of the team is to address a number of fundamental challenges so as to bring to fruition a physical computing system that can robustly accelerate a broad class of real-world problems. First, the funding will enable the team to build physical prototypes to better understand hardware non-idealities so as to develop cross-layer countermeasures for robust operation. Second, the project will make the first effort in addressing capacity limits and semantic gaps that are two major system-level issues confronting Ising machines. Third, the project will expand Ising machines beyond canonical problems, which can potentially transform them from a niche solution to a mainstream platform in critical application areas such as machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.