July 10, 2024 — The U.S. Department of Energy’s Advanced Scientific Computing Research (ASCR) program has announced a Monday July 22 deadline (11:59 pm ET) for position papers for a workship on neuromorphic computing for science.
The workshop website can be found here. Notification of position acceptance will be issues on Friday, Aug. 2. For meeting technical questions, please contact: Todd Munson, Todd.Munson@science.doe.gov
The 2024 Workshop on Basic Research Needs for Neuromorphic Computing, to be held Thursday and Friday, Sept. 12-13 in the greater Washington, DC area, will inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program.
Engineering novel neuromorphic computing systems with functionalities, capabilities, and energy efficiency similar to biological brains is one of the most exciting and challenging scientific endeavors of our time. This workshop aims to identify key research needs, challenges, and next steps necessary to develop biologically-realistic neuromorphic circuits primitives that capture the functionality of neural systems found in nature. Moreover, simulating neuromorphic computing primitives integrated into networks will be key to understanding their behavior at scale, particularly for those computing architectures where full-scale commercial fabrication is not yet readily accessible. Appropriate neuroscience datasets and metrics will have to be established to vet proposed neuromorphic circuits.
In the development of new circuits and methodologies for neuromorphic computing, it is critical that there is close collaboration among circuit designers, computer engineers, computational neuroscientists, and algorithms and simulation researchers. This workshop aims to bring together a diverse range of experts across three complementary technical areas.
Submit position paper to the technical areas below:
- Neuroscience algorithms and translation to neuromorphic analog circuits
This technical area is driven by the fundamental question “What are the key neuromorphic circuit primitives that are needed to capture the full functionality of critical biological computing mechanisms?”. The goal of the activities in this space is to understand what principles and circuit structures of brain organization and dynamics underpin its functionality and robustness capabilities and how these principles can be translated into functionally-equivalent neuromorphic circuits and systems that could be practically implemented (with available technology?). Ideas related to neuromorphic computing principles inspired from brain regions/functions (cortical, hippocampus, thalamus, sensing, motor control, etc.) are sought after. Topics related to neuromorphic approaches and emulations of small invertebrate brains are also of interest. - Technologies and prototyping of neuromorphic analog primitives
This technical effort is driven by the fundamental question “What are the technologies needed to demonstrate and prototype key neuromorphic circuit primitives?” Ideas related to novel neuromorphic circuits based on new devices and designs, and new principles guided by neuroscience-inspired functionality are of interest. Ideas related to emerging analog technologies that provide orders of magnitude in performance, parallelism, energy efficiency, tunability range, temporal delays, etc., and that mimic the biological behavior and robustness of key primitives are welcomed. Also of interest are topics related to high neuromorphic connectivity capabilities, e.g. optoelectronic technologies and photonic interconnects. - Scalable integration for neuromorphic computing modeling
The fundamental question driving this technical area is “What are the critical characteristics for effective large-scale simulation of neuromorphic circuits and systems?” New approaches are needed to create simulations of large-scale biologically-realistic neural networks, diverse synapse connectivity, and sophisticated network activity. Of interest are ideas related to novel methods to integrate and to scale up the simulation of the neuromorphic circuit primitives using high-performance computing in order to understand their interactions in the context of hundreds of millions of neurons and synapses. Also welcomed are novel methodologies for the efficient exploration of the large co-design space between neuromorphic algorithms and circuit technologies.
When discussing the technical idea and how it fits in the technical area(s) and the overall vision of the workshop, include a discussion on the benchmarks, metrics, and/or datasets requirements for neuromorphic computing for your proposed implementation.
Submission Guidelines
The structure for the ideal position paper may include several of the below themes:
- Neuroscience-inspired computing principles
- Translation to analog microelectronic circuits
- Modeling and simulation approaches
- Performance metrics, data requirements, and energy efficiency
The position paper should be an individual submission, one paper per investigator. The format is one page (plus one extra page for figures, captions and references only) with an 11-point font, submitted in a Word or PDF document. The primary theme should be mentioned during the submission, a secondary theme is optional.
Accepted position papers will be made public.
The following information is being collected during the submission process:
- Author email
- Author first name
- Author last name
- Author organization
- Position paper title
- Position paper theme(s)
- Position paper abstract
- Position paper file
Submissions will be reviewed by the workshop’s organizing committee using criteria of overall quality, relevance, likelihood of stimulating constructive discussion, and ability to contribute to an informative workshop report. Unique positions that are well presented and emphasize potentially transformative research directions will be given preference.
Workshop Organizers
Co-Chairs
- Gina Adam, George Washington University
- Garrett Kenyon, Los Alamos National Laboratory
- Thomas Potok, Oak Ridge National Laboratory
Organizing Committee
- Giorgio Ascoli, George Mason University
- Frances Chance, Sandia National Laboratory
- Yiran Chen, Duke University
- Joseph Friedman, University of Texas at Dallas
- Cory Merkel, Rochester Institute of Technology
- Maryam Parsa, George Mason University
- Midya Parto, University of Central Florida
- Catherine Schuman, University of Tennessee Knoxville
- Shinjae Yoo, Brookhaven National Laboratory
- Yuping Zeng, University of Delaware
