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
WARD, a runtime-adaptive Vision Transformer framework that combines channel-wise subnetwork partitioning, reliability-aware continual learning, and dynamic operating-mode scheduling to jointly optimize performance, fault tolerance, and adaptation according to runtime conditions.
Two physically isolated subnetworks execute under four operating modes (i.e. Full-Precision Mode, Low-Power Mode, High-Reliability Mode, and Adaptive Mode) that dynamically adjust computational cost and reliability while ensuring uninterrupted inference for real-time requirements.
To validate the practical deployability of the proposed framework
WARD is implemented on a lightweight FPGA-based accelerator extended with runtime hardware support for mode scheduling and resource management.
Experimental results demonstrate that the proposed split architecture achieves a network-level failure rate of only 1.79% under high Bit Error Rates.
The hardware implementation incurs less than 5% area overhead and supports runtime mode transitions within few clock cycles, demonstrating that adaptive reliability management can be integrated into programmable edge AI accelerators with negligible implementation overhead.