dAIEDGE will participate in the upcoming HiPEAC conference, one of Europe's most prestigious platforms for discussing and advancing technology, taking place in Barcelona (Spain), from 20 to 22 January 2025.
As part of the event, dAIEDGE is organizing:
- Workshop Excellence in AI and Edge Computing, which will be held on Monday, 20 January, from 10:00 to 17:30.
- Workshop on Accelerated Machine Learning (AccML), which will be held on Tuesday, 21 January, from 10:00 to 17:30
Call for Papers for the "Excellence in AI and Edge Computing" Workshop
This workshop will focus on exploring the synergy between artificial intelligence and edge computing, with an emphasis on developing scalable and robust systems. It will also provide a space for the academic and industrial community to discuss technical advancements, challenges, and innovative solutions in the field of AI at the edge.
The workshop will also feature two key components to make it more dynamic and interactive:
- Matchmaking Session: Participants will have the opportunity to interact and connect with other researchers and professionals in the field, fostering future collaboration.
- Demonstration Session: Attendees will have the chance to showcase their results through live demos, which can be presented in person or via video.
Awards for Best Contributions
To recognize the effort and quality of the contributions, awards will be given to the best works presented at the workshop:
- Best Paper Award: Diploma of recognition and a prize of €300.
- Best Demo Award: Diploma and €300 for the best demonstration in the demo session.
The deadline to submit your paper is 15 November 2024
For more information on paper submission, requirements, or workshop topics, visit: https://www.excellence-in-edge-ai.eu.
Call for Papers for the "7th Workshop on Accelerated Machine Learning (AccML)" Workshop
Papers will be reviewed by the workshop's technical program committee according to criteria regarding the submission's quality, relevance to the workshop's topics, and, foremost, its potential to spark discussions about directions, insights, and solutions in the context of accelerating machine learning. Research papers, case studies, and position papers are all welcome.
In particular, we encourage authors to submit work-in-progress papers: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work in order to elicit feedback, discover collaboration opportunities, and spark productive discussions.
Deadline: November 18, 2024.