RTML: Small: Design of System Software to Facilitate Real-Time Neuromorphic Computing

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Machine learning methods such as neural networks have been successfully used in real-time computer vision and signal processing areas. Neuromorphic systems, which mimic biological neurons and synapses, can be used to implement these neural networks in energy-constrained computing platforms. However, due to the absence of a user-friendly programming interface, the use of neuromorphic systems is currently limited to research. This project will develop such an interface, allowing for these systems to be more easily programmed and used by the broader science and engineering community within the U.S. The project will integrate undergraduate education within multidisciplinary graduate research, with students at all levels and across different disciplines participating in the proposed research activities. High school girls will participate in workshops during the summer, learning how to program robots equipped with neuromorphic hardware. The open-sourced programming tools will enable faster development and commercialization of neuromorphic systems in the U.S. and facilitate collaboration with other such communities worldwide.Executing a program on a computer involves several steps: compilation, resource allocation, and run-time mapping. Although very well defined for mainstream computers, no prior work has investigated these steps in a systematic manner for neuromorphic systems. This project will develop compiler tool chains to translate a user's machine learning program to low-level languages that can be interpreted by neuromorphic systems. A key initiative is to develop a common representation across different platforms. Resource optimization strategies will be developed to improve program performance; as well as an Operating System like framework that will allow programmers to easily deploy their machine learning programs on neuromorphic systems. The technical contributions will be demonstrated using two case studies: real-time sleep apnea detection and real-time image segmentation from video.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.