E2ML: Second Workshop on Energy-Efficient Machine Learning

Country: USA

City: Virtual

Abstr. due: 01.08.2020

Dates: 19.10.20 — 22.10.20

Area Of Sciences: Technical sciences;

Organizing comittee e-mail: spudukot@gmu.edu

Organizers: Sai Manoj P D, George Mason University


Advances in 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 manage the limited available resources. The second edition of the 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:

- In-memory computing 

- Neuromorphic computing

- Approximate computing for ML applications 

- Power management for ML architectures

- Emerging memory technologies and its applicability in ML applications 

- Spiking neural networks 

-  Learning algorithms on embedded systems 

- Hardware-software cross-layer co-design 

- Distributed ML algorithms and hardware for real-time performance

Any other relevant topic related to the 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. 

Conference Web-Site: http://mason.gmu.edu/~spudukot/E2ML_T.html