Workshop on

Neuromorphic Computing: from Material to Algorithm (NeuMA)

18 October 2021

8:00 AM - 11:45 AM (Pacific Time)

Future computing systems are expected to exploit our improved understanding of the brain through leveraging similar computational principles. In this way, Neuromorphic computing has shown the potential for breakthroughs in machine intelligence. The main goal of this workshop is then to dive deep into the rapidly developing field of Neuromorphic Computing and cover cross-layer design challenges from device to algorithms. The NeuMA brings together leading researchers from multiple disciplines and prominent universities and national labs to demonstrate new research and results, discuss future research needs, and shape collaborations. NeuMA also offers an opportunity to publish research in Neuromorphic Computing and associated domains.

Workshop Program

First Section (all in PT time):

Dr. Anup Das 8:15-8:45 Title: Design-Technology Co-Optimization for OxRRAM-based Neuromorphic Processing Elements

Dr. Jean Anne C. Incorvia 8:45-9:15 Title: Designing Magnetic Synapses and Neurons for Application-Specific Functions [slides]

Dr. Jae-sun Seo 9:15-9:45 Title: Fully Spike-based Architecture with Front-end Dynamic Vision Sensor and Back-end Spiking Neural Network

15 Minute Break

Second Section (all in PT time):

Dr. Yang (Cindy) Yi 10:00-10:30 Title: Approaching the Area of Neuromorphic Computing Circuit and System Design [slides]

Dr. Catherine Schuman 10:30-11:00 Title: Co-Design of Neuromorphic Systems from the Algorithms and Applications Perspective [slides]

Dr. Priyadarshini Panda 11:00-11:30 Title: Towards Energy-Efficient, Interpretable and Robust Neuromorphic Computing: Algorithm and Systems Perspective

The Speakers

Anup Das (8:15 AM PT)

Dr. Anup Das is an Assistant Professor at Drexel University. He received a Ph.D. in Embedded Systems from National University of Singapore in 2014. He received the Singapore President Fellowship for excellence in research and studies. Following his Ph.D., he was a post-doctoral fellow at the University of Southampton, UK and afterwards a researcher at IMEC, Netherlands, leading key projects related to neuromorphic computing. He received the NSF/DARPA real-time machine learning (RTML) award in 2019 for developing “operating systems” like framework for neuromorphic hardware. He is a recipient of the NSF CAREER award in 2020 and the DOE CAREER award in 2021 to address the security and dependability aspects of neuromorphic computing both from software and hardware perspective. He is a senior member of the IEEE.

Jean Anne C. Incorvia (8:45 AM PT)

Dr. Jean Anne C. Incorvia is an Assistant Professor and holds the Fellow of Advanced Micro Devices (AMD) Chair in Computer Engineering in the Department of Electrical and Computer Engineering at The University of Texas at Austin, where she directs the Integrated Nano Computing (INC) Lab. Dr. Incorvia develops practical nanodevices for the future of computing using emerging physics and materials. She received her Ph.D. in physics from Harvard University in 2015, cross-registered at MIT. From 2015-2017, she completed a postdoc at Stanford University in the department of electrical engineering, working in nanoelectronics. She received her bachelor’s in physics from UC Berkeley in 2008.

Jae-sun Seo (9:15 AM PT)

Dr. Jae-sun Seo received the B.S. degree from Seoul National University in 2001, and the M.S. and Ph.D. degree from the University of Michigan in 2006 and 2010, respectively, all in electrical engineering. He spent graduate research internships at Intel circuit research lab in 2006 and Sun Microsystems VLSI research group in 2008. From January 2010 to December 2013, he was with IBM T. J. Watson Research Center, where he worked on cognitive computing chips under the DARPA SyNAPSE project and energy-efficient integrated circuits for high-performance processors. In January 2014, he joined ASU as an assistant professor in the School of ECEE. During the summer of 2015, he was a visiting faculty at Intel Circuits Research Lab.

Yang (Cindy) Yi (10:00 AM PT)

Dr. Yang (Cindy) Yi is an Associate Professor in the Bradley Department of Electrical and Computer Engineering (ECE) at Virginia Tech (VT). Prior to joining VT, she has been working on various research topics in the area of Integrated Circuits and Systems (ICS) at Texas A&M University (TAMU), University of Kansas (KU), University of Missouri - Kansas City, Freescale, IBM, Intel, and Texas Instruments (TI). She obtained her Ph.D. in Electrical and Computer Engineering at Texas A&M University, the M.S. and B.S. in Electrical Engineering at Shanghai Jiao Tong University. Yang (Cindy) Yi has more than 100 publications in international journals and conference proceedings.

Catherine Schuman (10:30 AM PT)

Dr. Catherine (Katie) Schuman is a Liane Russell Early Career Fellow at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of models and algorithms for neuromorphic computing as part of her fellowship at ORNL.

Priyadarshini Panda (11:00 AM PT)

Dr. Priya Panda is an assistant professor in the electrical engineering department at Yale University, USA. She received her B.E. and master’s degree from BITS, Pilani, India in 2013 and her PhD from Purdue University, USA in 2019. During her PhD, she interned in Intel Labs where she developed large scale spiking neural network algorithms for benchmarking the Loihi chip. She is the recipient of the 2019 Amazon Research Award. Her research interests include- neuromorphic computing, energy efficient deep learning, adversarial robustness, and hardware centric design of robust neural systems.

Paper Submission Guidelines:

Electronic submissions of full papers (regular as well as invited) in PDF format are sought. All submitted papers will be reviewed and evaluated on correctness, originality, technical strength, significance, quality of presentation, interest, and relevance to the scope of the workshop. Submitted manuscripts may not exceed a total of 8 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, references, etc.​

Paper submission has to be done via EasyChair ( Select the separate track "Neuromorphic Computing: From Material to Algorithm (NeuMA)" to submit your paper.

Important Dates:

Paper Submission Deadline: September 15, 2021 (AoE) September 25, 2021 (AoE)

Acceptance Notifications for Papers: September 28, 2021 October 5, 2021 (AoE)

Guidelines for Registration:

  • Registration is required for all invited and regular speakers, as well as attendees.

  • Please note that for consideration of inclusion in the proceedings (IEEE Xplore), each accepted paper requires a conference registration at full speaker rate (USD 350 for IEEE members and USD 400 for non-members), plus an expected presentation of the paper at the NeuMA workshop.

  • Invited speakers with no paper(s) in the proceedings (IEEE Xplore) can register at a discounted rate (USD 200).

  • More information on registration can be found here.

Guidelines for IEEE Copyright Transfer (for Papers to Appear on IEEE Xplore) :

Due to COVID-19 related delays in finalizing the conference program:

  • We will temporarily make a PDF copy of the Workshop Proceedings (without copyright form collection) available on the IGSC website during the conference so that the conference registrants can have access to the papers.

  • Once the IEEE copyright forms are ready, we will be in touch to collect the final manuscripts and signed copyright forms in order to have the IGSC Workshop Proceedings available on IEEE Xplore.

Workshop Organizers:

Arman Roohi, University of Nebraska-Lincoln

Shaahin Angizi, New Jersey Institute of Technology