DOE SC ASCR Grant: Advancing Neuromorphic Computing for Scientific Discovery

DOE/NNSA National Laboratories are eligible to apply. Successful applications from DOE/NNSA National Laboratories will be authorized and funded under the DOE field work authorization system and performed under the applicable laboratory’s Management and Operating (M&O) contract.

Other Federal agencies and their FFRDCs are not eligible to receive financial assistance awards and are therefore ineligible to submit applications.

Applications from for-profit organizations that propose research related to current commercial activity may be declined without merit review.

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.