ABSTRACT

Machine learning has proved to be immensely successful across a range of social domains such as healthcare, environment, education, infrastructure, and cybersecurity. Computing platforms currently used to run machine-learning tasks have a high carbon footprint associated with them. Neuromorphic computing systems, which mimic biological neurons and synapses can implement these tasks in a highly energy-efficient fashion. Major challenges for neuromorphic computing, however, lie in its adoption by users and from a system developer's perspective, to cope with faster time-to-market pressure for new neuromorphic chip designs. This project develops a software infrastructure called NeuroXplorer, which helps both end-users as well as developers of neuromorphic systems: it allows for machine-learning tasks to be mapped onto neuromorphic chips in the most efficient way possible; and provides analysis, simulation, and synthesis tools that can be used to explore new chip designs to meet the needs of emerging machine-learning workloads. NeuroXplorer is distributed under an open-source license to promote the adoption of neuromorphic computing as well as the development and commercialization of neuromorphic systems in the United States.

The intellectual merits of the project lie in the development of compiler backends within NeuroXplorer to generate executable code for neuromorphic chips such as Loihi, Dynamic Neurormorphic Asynchronous Processor, and Microbrain from a high-level specification of the machine-learning task; development of mapping and synthesis tools to execute machine-learning tasks on novel neuromorphic architectures built using Field-Programmable Gate Array (FPGA); and development of high-performance software for hardware/software design-space exploration of new neuromorphic architectures. NeuroXplorer is built to be modular and extensible such that developers can easily contribute new features to the software. The capabilities of NeuroXplorer are accessible over the Internet. The end-user trains the machine-learning model using a standard workflow and uploads it, upon which the appropriate code is automatically generated and executed on neuromorphic architecture. The neuromorphic program and bitstream files for the final FPGA design can be freely downloaded. Design-space exploration tools within NeuroXplorer efficiently tackle the growing complexity of neuromorphic systems and challenges in integrating emerging design technologies into these systems. From an educational perspective, the project involves both graduate and undergraduate students at Drexel University in the development of the software. Collaborators from academia and industry deliver guest lectures on current developments in neuromorphic hardware, system software, and applications, with these lectures being integrated within relevant courses.

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.

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