Call for Papers

3rd Workshop on Energy-Efficient Machine Learning (E2ML)

Print Version

This workshop is co-located with International Green and Sustainable Computing Conference (IGSC)


2021/07      Submission site has been opened at EasyChair. (Pleaes see details at Submission Guidelines).
2021/06      Due to uncertainties of the COVID pandemic, IGSC 2021 will be held as a Live, Video Streaming Virtual Conference
2021/06      E2ML Website is open now!

Scope of the Workshop

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:

  • 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
  • Quantum computing
  • Quantum machine learning

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.

Submission Guidelines:

There are two ways to participate the workshop: (1) Presentation with paper publication and (2) Presentation without paper publication. Please see details as follows.

Presentation with paper publication

Workshop papers will appear on IEEE library (IEEE Xplore). All papers must be original and not simultaneously submitted to another journal or conference. Full papers should be of length up to 8 single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style) including figures, tables, and references.

  • IEEE templates can be found here
  • Full paper submission deadline: August 23rd, 2021   September 6th, 2021
  • Notification of acceptance: September 6th, 2021    September 13th, 2021
  • Final paper deadline: September 13th, 2021    September 20th, 2021 (Firm Deadline)
  • Papers have to be submitted over EasyChair: Tack of Energy-Efficient Machine Learning.

Please select the correct track in EasyChair system

Presentation without paper publication

If you have interesting work related to the scope of the workshop and want to present at the workshop without publication, please feel free to directly contact Weiwen Jiang.

Schedule at 10/18/2021, based on Estern Time (Tentative)

Session 1: Energy Efficiency in Classical Computing

Time Author(s) Affiliation Title Type Resource
09:00-10:00 Alex Jones NSF CNS Program Director to be deteremined Keynote Speech
10:00-10:30 Caiwen Ding University of Connecticut to be deteremined Invited Talk
10:30-11:00 Ali Jahanshahi, Rasool Sharifi, Mohammadreza Rezvani and Hadi Zamani Sabzi University of California, Riverside & University of Virginia Inf4Edge: Automatic Resource-aware Generation of Energy-efficient CNN Inference Accelerator for Edge Embedded FPGAs Paper Presentation [Paper]

Session 2: Energy Efficiency in Quantum Computing

Time Author(s) Affiliation Title Type Resource
11:30-12:00 He Li, Hongxiang Fan and Jiawei Liang University of Cambridge & Imperial College London & Beihang University Quantum Most-Significant Digit-First Addition Paper Presentation [Paper]
12:00-12:30 Zhepeng Wang, Zhiding Liang, and Weiwen Jiang George Mason University & University of Notre Dame Co-Design of Quantum Neural Network and Quantum Circuit Invited Talk


All questions about submissions should be directed to Weiwen Jiang