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Special Session 3:
Neuromorphic Computing and Applications

Neuromorphic computing systems are a rapidly developing area of research that offer several key features that make them ideal for real-time and low-energy machine learning applications. Recent studies have demonstrated that neuromorphic systems can achieve a significant reduction in energy and power compared to traditional deep learning models, making them a highly promising alternative for a wide range of use- cases. At the core of neuromorphic systems are spiking neural networks (SNNs), which are specifically designed to mimic the behavior of neurons in the human brain. SNNs are highly effective at capturing temporal associations in data, making them an excellent choice for time series and dynamic image analysis. Moreover, SNNs are well-suited for applications where the model must be updated continuously as new data streams arrive. SNNs can learn incrementally and continuously, making them optimal candidates for continual learning. Overall, neuromorphic computing systems offer an exciting and innovative approach to machine learning, with the potential to transform a wide range of fields. As research in this area continues to progress, it is likely that we will see even more innovative solutions emerge, pushing the boundaries of what is possible with machine learning.

The special session is designed to provide a platform for researchers from both academia and industry to showcase the latest developments in the area of neuromorphic computing. This cutting-edge technology is becoming increasingly relevant in various fields such as robotics, healthcare, IoT, manufacturing, and more. The session will serve as an opportunity to explore the diverse range of applications of neuromorphic computing and its potential for revolutionizing these fields. Attendees can expect to witness presentations on breakthrough research, as well as demonstrations of practical implementations of the technology. By bringing together experts and enthusiasts from various disciplines, this special session aims to foster collab- oration and exchange of knowledge, as well as inspire further advancements in the field. Overall, it promises to be an informative and engaging event for anyone interested in the future of neuromorphic computing and its impact on various applications.

Scope and topics:

The special session welcomes original and novel research papers on several relevant topics including but not limited to:

• Supervised and Unsupervised Learning for SNNs • ANN to SNN Conversion
• Online Learning with SNNs
• Neuro-Evolution and Evolving SNNs
• Time Series Analysis with Neuromorphic Systems
• Neuromorphic Computing for Natural Language Processing • Spiking Transformer Models
• Neuromorphic Computing for Computer Vision
• End-to-End Neuromorphic Systems
• Neuromorphic Datasets
• Neuromorphic Tools and Simulation Frameworks

Chairs: Ramtin Zand, Jason Eshraghian

Bio: Dr. Ramtin Zand is an assistant professor of the Computer Science and Engineering department, and the principal investigator of the Intelligent Circuits, Architectures, and Systems (iCAS) Lab at the University of South Carolina. The iCAS lab has close collaborations with and supported by several multinational compa- nies including Intel, AMD, and Juniper Networks, as well as startups such as Van Robotics and ZKFlash. Dr. Zand has authored more than 50 journal and conference articles and two book chapters and received recognition from ACM/IEEE including the best paper runner-up of ACM GLSVLSI’18, the best poster of ACM GLSVLSI’19, and best paper of IEEE ISVLSI’21, as well as featured paper in IEEE Transactions on Emerg- ing Topics in Computing. His research focus is on neuromorphic computing, acceleration of data-intensive and compute-intensive applications, and real-time and energy-efficient machine learning. Dr. Jason Eshraghian is an assistant professor of Electrical and Computer Engineering at UC Santa Cruz. He is the developer of snnTorch, a widely adopted Python library used to train and model brain-inspired spik- ing neural networks. He was awarded the IEEE TCAS-I Darlington’23, IEEE TVLSI’19, and IEEE AICAS’19 best paper awards, and the best live demonstration award at IEEE ICECS’20. He was the recipient of the Fulbright Future Fellowship (Australian-America Fulbright Commission), the Forrest Research Fellow- ship (Forrest Research Foundation), and the Endeavour Fellowship (Australian Government). He leads the UCSC Neuromorphic Computing Group supported by Intel Labs and the NSF, focusing on advancing neuro- morphic computing research. Dr Eshraghian is the Secretary of the IEEE Neural Systems and Applications Committee, and an Associate Editor with APL Machine Learning.

Technical Committee

• Ramtin Zand, University of South Carolina
• Jason Eshraghian, University of California Santa Cruz
• Catherine Schuman, University of Tennessee at Knoxville
• Melika Payvand, University of Zurich
• Maryam Parsa, George Mason University
• Kenneth Stewart, Naval Research Lab

Paper Submission Instructions

All papers will be double-blind reviewed and must present original work.

• CMT Submission Site
• Select the track: Special Session 3: Neuromorphic Computing and Applications

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:

• IEEE website

Keydates

• Submission due date: September 5, 2023
• Notification of Acceptance: September 25, 2023
• Camera Ready Papers: October 5, 2023
• Pre-registration: October 15, 2023
• Conference: December 15-17, 2023

Registration

In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.

Paper Presentation Instructions

The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.