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Project: NXP: On-device learning/federated learning on limited resource devices
Description
- On-device learning/federated learning on limited resource devices
- Addressing the following questions from NXP perspective:
- What is the latest state of the art for embedded on-device learning?
- How does on-device training differ from a regular (desktop/cloud) backprop-setup?
- Are approaches using one/few-shot training efficient and competitive?
- What deployment toolchains exist for efficient embedded on-device learning?
- What are the requirements from a HW-perspective?
- What are opportunities for improving on-device learning through federated learning?
Contact the TU/e supervisor (Joaquin Vanschoren) if interested. Acceptance will depend on acceptance by NXP and the availability of supervison.
Details
- Supervisor
-
Joaquin Vanschoren
- External location
- NXP
- Interested?
- Get in contact