Advances in the machine learning (ML) and its deployment in a wide range of systems for various applications. This has stirred interest in the design of various devices ranging from cloud servers to miniature IoT devices equipped with smart capabilities with embedded ML. One of the major hurdles to be addressed for an efficient design of such systems is to managed the limited available resources. The second edition of Workshop on Energy-Efficient Machine Learning (E2ML) workshop focuses on the design strategies to minimize the footprint and efficient management of resources through advanced computing techniques as well as resource management. The topics of interest to this workshop are:
Any other relevant topic related to design of hardware for ML and optimizing ML for resource constrained systems is within the scope of the workshop. Papers that showcases interesting analysis regarding embedded ML and the possible ways to extend are also welcome.
All papers must be original and not simultaneously submitted to another journal or conference. Full papers should be of length up to 6 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style) including figures, tables, and references.
The workshop proceedings will be published in IEEE Explore.
Given these unprecedent circumstances, the IGSC and E2ML workshop will happen virtually. Stay tuned for more details.
All questions about submissions should be directed to Sai Manoj P D
Sai Manoj P D, George Mason University