Physics-Inspired Compute Engines for Accelerating Combinatorial Optimization
Nikhil Shukla, Ph.D.
This project aims to advance physics-based solvers for complex computational problems in combinatorial optimization.
The goal is to make these solvers more suitable for commercial applications, particularly in energy-constrained environments, by adjusting algorithms and optimizing hardware-algorithm co-design. The approach is inspired by natural systems and uses unique physics-inspired algorithms and hardware, offering the potential for significant performance improvements over existing methods. This technology has applications in resource allocation, autonomous vehicles, artificial intelligence, finance, and more, making it valuable for various industries, including discussions with partners like Northrop Grumman.
