Important Dates

Workshop Paper Submission Due:  April 5 15, 2023
Acceptance Notification:                  April 21, 2023
Author Registration Deadline:         April 24, 2023
Final Version Submission Due:       May 1, 2023
Early Registration:                           TBA
Workshop Day:                                 TBA

Scope and overview

Machine learning (ML) systems are gaining immense popularity and are increasingly deployed in edge computing devices and networks. These devices are characterized by limited computing capabilities, being also restricted by power constraints. Similarly, edge-first networks face delays, jitter, and packet losses due to resource contention, high traffic loads, and other reasons. ML can be used to process and analyze data from edge devices and sensors to extract useful information and insights. This information can then be used to make decisions about how to manage and optimize at run-time both the edge devices and the network. Additionally, ML can help to identify patterns and correlations in data, which can be used to improve decision-making. However, there are a number of challenges associated with implementing machine learning on edge devices and networks.

The RCML Workshop aims to stimulate research on the latest advancements in resource-constraint machine learning for edge systems. Research results from funded projects in the general area of machine learning optimizations for edge computing are especially encouraged. Overall, the workshop seeks original manuscripts in the scope of the workshop, but not limited to:

• Resource-aware machine learning algorithms
• Energy-efficient hardware accelerators for machine learning
• Machine learning for edge-first networks
• Resource management in edge-first networks
• Approximate computing
• Reconfigurable systems
• Methods for machine learning optimization and compression
• Emerging design technologies for future computing
• Power-efficient and sustainable computing
• Internet of Things
• Case studies of machine learning for edge systems
• Protocols for communication in edge-first networks
• End-to-end protocols, flow and congestion control
• Pervasive and wearable computing and networking
• Artificial intelligence and machine learning for wireless networks
• Attack modelling, prevention, mitigation, and defense in wireless networks
• Reinforcement learning and deep learning for networks

Committees

Program Committee Chair: Iraklis Anagnostopoulos (Southern Illinois University Carbondale, USA)
Technical Program Committee: TBA