This Expeditions project seeks to gain a deeper understanding of the fundamental nature and potential uses of Coherent Ising Machines (CIMs). These machines exploit unconventional computing architectures to solve crucial optimization problems for application domains ranging from logistics and robotics to materials engineering and drug design. Based on the performance of current CIM prototypes, next-generation CIMs hold great promise to drive substantial practical advances in artificial intelligence (AI) capabilities in such fields. CIMs are of significant fundamental research interest as well, as novel architectures with which we can test transformative ideas for computer engineering in the post-Moore’s Law era. CIMs exploit a synergistic combination of optical and electronic components to achieve both massive data connectivity and fast programmable logic. They likewise utilize an unconventional optical memory that represents a stepping stone towards more radical quantum information technologies. CIM prototypes today achieve substantially greater scale while leveraging more incremental advances in device physics than do prototype quantum computers. Work to develop new CIM applications, benchmark CIM prototypes, and analyze CIM scaling can thus shed new light on poorly understood aspects of the physics of computation that sit between conventional technology and an idealized quantum regime. CIMs rely upon data processing primitives with substantial parallels to those required for deep learning, suggesting that technical innovations arising from CIM research may have broader impact in hardware development for AI. The core research of this CIM Expedition will thus serve as an important complement to ongoing efforts towards quantum and neuromorphic computing.
This CIM Expedition will support the development of advanced prototype hardware incorporating recent advances in nanophotonics, optoelectronics and ultrafast laser sources. In continuing to explore the complementarity of optics and electronics for specialized optimization/AI architectures, its researchers will be particularly interested in assessing tradeoffs between raw speed and energy efficiency that can be made in new ways in this hybrid design space. The project will investigate generalizations of CIM architectures utilizing insights from reservoir computing and message passing algorithms, and establish a general theory of the role of quantum effects in CIM including strategies to exploit them robustly. The project will extend recent theoretical analyses of the dynamics of deep learning neural networks to elucidate subtle connections between the mathematical structure of hard optimization problems and the solution trajectories of physical computing machines. The CIM Expedition team will work with industrial partners and applications-domain specialists to perform extensive benchmarking of existing CIM prototypes, forecasting feasible CIM performance in comparison with conventional computing approaches as well as emergent quantum computing. This work will follow best practices for making comparisons across disparate hardware platforms and realistic application use cases in combinatorial optimization and machine learning, established previously by members of the CIM Expedition team. Overall the project will aim to develop a sharper understanding of the capabilities and operating principles of current CIM prototypes, as well as a clearer picture of future possibilities for CIM scaling and fundamental performance improvements.
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.
