ASCENT: Using Optical Frequency Comb for Ultrafast Nature-Based Computing for Machine Learning Algorithms

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Expanding the boundaries of current computing system performance calls for disruptive innovations to enable next-generation architectures beyond the traditional, so-called von Neumann paradigm. In this project, a novel photonic non-von Neumann system will be developed that pushes the envelope of nature-based computing in efficiency, capability, and applicability. Products and insights from this ASCENT collaboration have strong transformative potentials to bring nature-based computing to the state of compelling infrastructure and directly impact the gamut of application domains of machine learning (ML) in scientific discovery, industry, assistive technologies, robotics-aided healthcare, economic development, and consequent improvements in quality of life. Envisioned broader impacts will permeate to the integrated photonics community, with new functions being realized at the chip level for microcombs that can serve as key enablers in new sensing and communication platforms. Project outcomes will generate new knowledge and disruptive innovation for hybrid photonic and electronic interfaces; enable systems and architectures beyond the von Neumann paradigm, and thus impact Future Semiconductor Technology (FST) platforms -- a strategic national priority; and train next generation engineers for continued innovation in this area.While nature-based, non-von Neumann computing machines such as D-Wave’s quantum annealers are showing promise, these current machines are far from compelling due to their demanding (e.g., cryogenic) operating conditions, significant bulk, their relatively high energy consumption, and their limited applicability to combinatorial optimization problems. They will only be truly viable when they are significantly more capable and efficient than state-of-the-art von Neumann platforms in solving a non-trivial section of real-world problems. This project’s ambitious and broad vision is to bring such a machine to fruition, which can only be realized via convergent research in devices, circuits, algorithms, and ML. Nanophotonics is a promising direction to catalyze the required transformative advances, through an optical frequency microcomb that can be controlled to function as a large-scale computing system. A microcomb-based non-von Neumann system will be developed, which can accelerate a variety of ML algorithms. Building medium- to large-scale system prototypes calls for developments in physical hardware for learning systems, integration of silicon photonic circuits exploiting microcombs, and co-design of novel ML algorithms that leverage the unique features of this machine. This project will educate and train the next generation of researchers to think outside the box of their niche discipline, instill in them the excitement of crossing disciplinary boundaries, and give them first-hand appreciation of the need for convergent efforts towards making progress in engineering system applications with high societal impact.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.