We are entering an era full of modern AI-powered mobile/IoT applications with prominent examples like virtual/augmented reality and autonomous driving. Leveraging sophisticated machine learning technologies, these applications typically require massive computation applied on an enormous amount of data (e.g., audio/video and time-series sensor data). On the other hand, such applications impose stringent constraints on performance metrics like latency, throughput, and reliability due to their user-interactive and mission-critical nature. These challenges have driven the evolution of the traditionally centralized cloud computing paradigm into a more distributed one at the network edge in close proximity of end-devices or even on-device computing solutions, where energy efficiency is of critical concern.

WEEE focuses on efficient machine learning solutions for mobile/IoT scenarios and energy-efficient systems for edge computing and networking in general. We invite researchers and practitioners to submit original ongoing work, visionary ideas, or experience related to topics include, but not limited to:

- Energy-efficient machine learning at the edge
- Mobile deep learning / augmented reality / virtual reality
- Efficient edge video analytics systems
- Machine learning on embedded/IoT devices
- Energy-efficient computer architectures for edge computing
- Energy-efficient edge network protocols/systems
- Energy-efficient in-network computing with programmable network devices
- Characterization of edge machine learning workloads
- Low-power sensing and computing
- Efficient blockchain/distributed ledger solutions on mobile/edge platforms

Submissions focusing on specific mobile/IoT applications and generic edge computing are both welcome. We solicit the following two types of contributions:

- Regular papers describing original research ideas and work, up to 6 pages including all figures, tables, but excluding references. Accepted regular papers will be presented at the workshop as oral presentations.
- Vision papers identifying new research problems/challenges in the field, up to 4 pages including all figures, tables, but excluding references. Accepted vision papers will be presented at the workshop as short presentations followed by interactive discussions.