SBIR Phase I: Superconducting Neural Units with Adjustable Weights

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The broader impact of this Small Business Innovation Research (SBIR) Phase I project aims to develop a new neuromorphic computing platform, the effects of which may be seen in many different fields, ranging from the automobile industry to robotics and biomedical applications. The reduction in energy use afforded by the technology could save up to 2% of the total electricity consumption nationwide, making it one of the most environmentally friendly computing platforms. Furthermore, the superconductor-based hardware has the potential to provide one of the fastest commercialized high-performance computing platforms for both industry and research, offering the potential to solve current computational problems in many fields. The development of high-frequency neuromorphic computing, with specific application to artificial intelligence applications, promises to strengthen the US economy. This Small Business Innovation Research (SBIR) Phase I project aims to bring to the technological forefront a high-speed superconductor electronics-based neuromorphic computer centered around neural units with adjustable weights serving as a disordered array. Currently, high-speed computing necessary for large artificial intelligence models is bottlenecked at semiconductor device architecture as Moore’s Law reaches its limits. Much of the necessary computing power is only accessible via paid run time on vast server banks owned by a select few companies that can afford to build them. By unlocking computing power based on SQUID (superconducting quantum interference device) arrays and rapid single flux quantum (RSFQ), the project aims to develop an affordable, reliable, and carbon friendly high-speed computing platform, thereby helping to solve the power/cost crisis facing currently accessible computing systems.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.