# WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI

arXiv: https://arxiv.org/abs/2609.17556 · HTML: https://mlsanity.com/paper/2609.17556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Soft Error Reliability Analysis of Vision Transformers](https://mlsanity.com/paper/2302.10468/) (2023)
- [Efficient Model Adaptation for Continual Learning at the Edge](https://mlsanity.com/paper/2308.02084/) (2023)
- [Safe-NEureka: a Hybrid Modular Redundant DNN Accelerator for On-board Satellite AI Processing](https://mlsanity.com/paper/2602.04803/) (2026)
- [Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks](https://mlsanity.com/paper/2407.10758/) (2024)
- [The Anatomy of Silent Data Corruption: GPU Error Pattern Study and Modeling Guidance](https://mlsanity.com/paper/2605.04213/) (2026)
- [SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors](https://mlsanity.com/paper/2606.07620/) (2026)

## Abstract (arXiv)

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 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.