Research program 03
Edge and Offline AI
Efficient models and runtimes designed to operate locally where bandwidth, latency, power, privacy or reliability make continuous cloud access unsuitable.
Research programInternal
Research objective
Make multimodal intelligence useful on constrained hardware and intermittent networks without concealing the quality and safety tradeoffs introduced by compression.
01 / Core scientific questions
Questions under investigation.
- Which capabilities can be retained under strict memory, energy and latency budgets?
- When should computation remain local, defer, or move to the cloud?
- How can offline systems update safely and preserve provenance?
02 / Current projects
Research topics.
- Model compression
- Quantization
- On-device multimodal inference
- Hybrid edge-cloud systems
- Energy-aware computing
- Secure offline operation
03 / Methods
Intended methods.
- Quantization-aware evaluation
- Runtime profiling
- Edge-cloud partition studies
- Failure-mode testing under degraded connectivity
04 / Evaluation
How the program will be evaluated.
- Capability retained after compression
- Energy and latency envelopes
- Offline task completion
- Recovery after interrupted synchronization
Evaluation protocols and results will be published only after review. No performance result is claimed on this page.
05 / Research infrastructure
Systems and related models.
06 / Verification pending
Researchers and related publications.
Researchers involved
Verified researcher profiles have not yet been published.
Related publications
No public publication is currently associated with this program.