Analyzing and Mitigating Circuit Aging Effects in Deep Learning Accelerators

The widespread adoption of Deep Neural Networks (DNNs) can be attributed to their remarkable performance in tackling complex real-world problems. Consequently, they have found extensive use in everyday applications as well as in high-assurance environments. Nonetheless, various challenges undermine the reliability of these DNNs in mission-critical scenarios. One such challenge is circuit aging, an inevitable consequence of prolonged usage leading to the deterioration of circuit performance. Therefore, it is of utmost importance to grasp the implications of circuit aging at the application level and to adopt proactive strategies for mitigating these effects. Towards this end, our paper examines the adverse effects of circuit aging on the performance of DNN applications and introduce a novel aging-aware training (AAT) framework to mitigate such detrimental impacts. To the best of our knowledge, this framework is the first of its kind, expressly tailored to train models while considering the impact of aging. Additionally, to extend the operational lifespan of the system, as opposed to its immediate disposal, we advocate a strategic model replacement approach based on a performance threshold, particularly when aging becomes a prominent concern. Through extensive experiments involving cutting-edge DNN models, we observe substantial performance enhancements of up to 78% when utilizing AAT, even in the presence of aging, as compared to training without AAT. The model replacement approach yields significant results as well, exhibiting up to 30% relative improvement in accuracy when subjected to the same application workload. Furthermore, this improvement is augmented with AAT, achieving an additional 20% improvement, demonstrating the efficacy of the proposed framework.