We are interested in work that discusses the challenges and opportunities for using machine learning in resource constrained environments. We define resource constrained broadly to refer to communities with limited resources required for machine learning training (limited compute) and/or inference (limited internet connectivity, high sensitivity to data prices or low bandwidth). The work can address any of the following:
- An applied problem (discussion of challenges and opportunities deploying models to resource constrained environments),
- General solutions to improve the efficiency of models (e.g. more efficient data sampling, algorithmic solutions like model pruning, quantization, federated learning, hardware solutions, IOT, efficient data sampling)
- Or an open source engineering contribution (e.g. releasing open source code that helps the general community in some way).
Please use the form below to submit your talk.
Successful applicants will each give a 8 minute oral presentation (with slides) followed by 2 minutes of questions from the audience. When making your slides, remember that you will only have 8 minutes to present, so less than 10 and at most 15 slides are recommended. Slides can be in Microsoft Powerpoint, a link to Google slides or a PDF of the slides.
As a speaker, you will have the opportunity to have your work profiled and engage with fellow researchers. Need more guidance? See some of the spotlight talks profiled in the previous ml at edge workshop.
- Submission Deadline : Monday August 1st 2022
- Notice of Acceptance : Monday August 15th 2022
- Workshop Date : Friday August 26 2022