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The 1st Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML) focuses on work that combines sensor signals from the physical world with machine learning, particularly in ways that are distributed to the device or use edge and fog computing. The development and deployment of ML at the very edge remains a technological challenge constrained by computing, memory, energy, network bandwidth and data privacy and security limitations. This is especially true for battery operated devices and always-on use cases and applications. This workshop will provide a forum for sensing, networking and machine learning researchers to present and share their latest research on building machine learning enabled sensor systems. Sensys-ML focuses on providing extensive feedback on Work In Progress papers involving machine learning (TinyML/ UltraML) on sensor systems.
Topics of interest include, but are not limited to, the following:
Advancement in Hardware for enabling TinyML capabilities at the edge
System Architecture for supporting TinyML and UltraML
Parallel and Distributed Machine Learning for Sensor and Network systems
Machine Learning driven Data Analytics
System and Algorithm co-design for practical TinyML at Sensor Systems
Security and Privacy at the Edge
Video Analytics at the Edge
Validation and debugging of TinyML and UltraML
Emerging Sensing Applications using TinyML
Submitted papers must be unpublished and must not be currently under review for any other publication. There are two submission tracks:
Full papers, which will be eligible for publication at most 6 single-spaced 8.5” x 11” pages with 10-pt font size in two-column format, including figures and tables and references. All submissions must use the LaTeX (preferred) or Word styles found here. LaTeX submissions should use the acmart.cls template (sigconf option), with the 10-pt font, make sure to use documentclass[10pt, sigconf]{acmart} in your document. This format will be used also for the camera-ready version of accepted papers. Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. We require each paper to be in Adobe Portable Document Format (PDF) and submitted through the Sensys-ML HoTCRP submission site. Accepted papers will be published in the ACM Digital Library. At least one of the authors of every accepted paper must register and present the paper at the workshop. The program committee will elect one paper for the Best Paper Award.
Work In Progress, which is a presentation-only format. We request a 1 single spaced 8.5” x 11” page abstract submission. Please note that these submissions will not be published as this is a presentation-only format.
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