WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI
Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation. Such conditions arise in long-running edge AI applications, including autonomous systems, industrial monitoring, and satellite onboard intelligence. Existing fault-tolerant methods assume static operating conditions, whereas continual learning techniques neglect concurrent hardware faults during online adaptation. Moreover, the practical deployment of runtime-adaptive reliability frameworks on programmable AI accelerators remains largely unexplored. This paper presents WARD, a runtime-adaptive Vision Transformer framework that combines chan...
| Authors | Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi et al. |
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| Primary category | cs.AR |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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