FET: Small: Heterogeneous Learning Architectures and Training Algorithms for Hardware Accelerated Deep Spiking Neural Computation

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This project aims to address the present performance and energy efficiency crisis in computing across broad areas of data-driven applications by developing energy-efficient new spiking neural architectures, training algorithms, and hardware computing devices. Inspirations from biological brains will be taken to support the development of algorithms and hardware systems to close the widening gap between the supply and demand of computing power. The outcomes from this project will be strongly interdisciplinary and are expected to stimulate technical advancements in machine learning and bridge between neural networks, neuroscience, and hardware engineering. The research will provide rich training and educational opportunities to students. Research participation from undergraduate students and underrepresented groups will be promoted through various outreach programs. The results of this project will be disseminated in broad research and industrial communities and integrated into the graduate-level curriculum. Research collaboration with industry will be sought to guide this work toward addressing real-world challenges and provide mentoring and training of students in the industrial setting. Brain-inspired models of computation and hardware computing systems hold the promise of delivering the amount of computing power required in processing increasingly large volumes of data in the post Moore's Law era, without a correspondingly high energy cost. This project will focus on improving the performances of spiking neural models for real-life learning tasks by addressing two pressing inter-dependent research roadblocks: lack of computationally powerful learning architectures, and lack of practical algorithms that can effectively train complex spiking neural models. Synergies between neuroscience and deep learning will be explored to develop heterogeneous deep spiking neural architectures and learning algorithms to address the corresponding training bottlenecks. Efficient spiking neural processors will be demonstrated on reconfigurable computing devices.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.