International Workshop on Resource-Constraint Machine Learning: Unlocking the potentials of edge computing devices and networks
Authors are invited to submit original unpublished manuscripts that demonstrate current research on resource-constraint machine learning targeting
edge computing devices related to RCML workshop's topics of interest. Please use the US letter size (8.5 X 11 in) standard IEEE conference LaTeX format or
Microsoft Word template available on this link.
All submissions should be written in English and submitted as a full paper. Full papers should have a maximum of six (6) printed pages including figures and references.
Full papers are expected to describe fully developed ideas with a thorough evaluation. Note that RCML does not follow the double-blind review policy.
The names and affiliations of all the authors must be present in the submitted manuscript.
Accepted and presented papers will be published in the HPSR 2023 conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases.
Please submit your papers in PDF format using the following paper submission website:
Submit paper via EDAS
TBA