About
Akul Mallayya Swami is an independent researcher and safety critical embedded systems engineer focused on deployment assurance, runtime trustworthiness, and timing validation for edge machine learning systems operating in real world environments.
His research investigates how AI systems can remain statistically correct while becoming temporally unreliable under deployment interference, a failure mode that is often under evaluated in current ML validation pipelines.
His current work focuses on hardware validated timing analysis, runtime timing fidelity monitoring, and interference aware deployment evaluation for edge AI systems using externally calibrated measurement methodologies.
Current Research
TFS Guard studies how deployed edge ML systems can preserve output correctness while violating timing assumptions under runtime interference. The work evaluates timing drift, deadline violation behavior, and hardware validated runtime observability.
Comparative experiments examine shared Linux inference environments and more isolated edge inference paths to evaluate how architecture affects temporal reliability under realistic contention.
Research Interests
Research and Publications
Research manuscripts and preprints focus on deployment timing validation, runtime trustworthiness, and externally calibrated timing measurement for edge ML systems.
Public repositories are being structured around experiment protocols, timing logs, analysis scripts, hardware calibration notes, and reproducible edge ML timing evaluations.
Experience
Education
Standards and Technical Focus