Position Paper: From Edge AI to Adaptive Edge AI
Edge AI is often framed as model compression and deployment under tight constraints. We argue a stronger operational thesis: Edge AI in realistic deployments is necessarily adaptive. In long-horizon operation, a fixed (non-adaptive) configuration faces a fundamental failure mode: as data and operating conditions evolve and change in time, it must either (i) violate time-varying budgets (latency/energy/thermal/connectivity/privacy) or (ii) lose predictive reliability (accuracy and, critically, calibration), with risk concentrating in transient regimes and rare time intervals rather than in average performance. If a deployed system cannot reconfigure its computation - and, when required, it...
| Authors | Fabrizio Pittorino, Manuel Roveri |
|---|---|
| Primary category | cs.AR |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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