Neuromorphic Computing for Accelerating Scientific Discovery

The summary for the Neuromorphic Computing for Accelerating Scientific Discovery grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Office of Science, which is the U.S. government agency offering this grant.

Neuromorphic Computing for Accelerating Scientific Discovery: The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in proposals for basic research that significantly advances Neuromorphic Computing as a brain-inspired, energy-efficient tool for scientific discovery. DOE has the responsibility to address the energy, environmental and nuclear security challenges that face our nation. SC's mission is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and national security of the United States. In the post-exascale computing timeframe, scientific progress will be predicated on our ability to create, transfer, and process large complex datasets from extreme scale simulations, experiments, and/or observational facilities. Scientific computing is facing multiple challenges i.e. high energy usage, memory, concurrency, parallelism, heterogeneity, input/output, storage, retrieval, fault tolerance, etc. Currently, high performance computing systems consume in the order of megawatts of power, and energy consumptions levels have been kept manageable through the usage of heterogeneous architectures. On the other hand, the energy consumption of the human brain is in the order of watts, or about six orders of magnitude more energy efficient than current state-of-the-art HPC systems. This program call is focused on high-risk, high-reward basic research to explore how neuromorphic computing could address emerging scientific computational challenges in energy efficient computing primarily.