E2CDA: Type I: EXtremely Energy Efficient Collective ELectronics (EXCEL)

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

With billions of smart and connected devices, "data deluge" is a reality with more than eight zettabytes of data generated in last year alone. The primary focus of this multi-disciplinary research effort is to develop a new paradigm of computing titled Extremely energy efficient Collective Electronics (EXCEL) to enable hardware accelerated data-analytics that can extract information from unlabeled and unstructured data. This research is expected to uncover fundamentally new ways of harnessing coupled dynamical systems for solving computationally hard problems in an energy efficient way. With innovations in novel materials and devices, chip-scale dynamical system implementation, architectural changes and critical benchmarking, EXCEL will lay the foundation for a new non von-Neumann computing paradigm to achieve orders of magnitude improvement in computational energy efficiency. The outreach activities are prioritized around educating future generations of students to adapt to the forthcoming evolution and revolution in information processing systems. The research project is structured to benefit from strong engagement with industry, which will facilitate technology transfer in the future. The participating PIs are committed to developing course modules for both undergraduate and graduate students in the areas of emerging nanotechnologies, unconventional computing, machine learning and computational medicine. The EXCEL project lays the foundation for a radically different approach to energy efficient information processing by leveraging emergent phenomena in novel devices and dynamics of coupled systems to execute optimization, learning and inference tasks in a collective, cooperative and scalable way. The intellectual foundation of EXCEL rests on the utilization of local information content embedded in the spatio-temporal dynamics of coupled networks (oscillatory and spiking) to perform computation in a massively parallel way. The collective computing paradigm is a timely departure from the traditional model of von-Neumann computing which relies on batch, discrete time, iterative updates (lacking temporal locality) and shared states (lacking spatial locality). EXCEL focuses on developing a complexity theoretic foundation in analog computing. This includes exploration of both continuous and discrete optimization problems as well as stochastic machines for on-line learning. The EXCEL researchers will actively pursue physical hardware demonstration and quantify their advantages over Boolean computers in solving computationally hard problems that are finding ever expanding applications in high-performance data centers, real-time cyber-physical systems and computational medicine.