Low-overhead Aging-Aware Resource Management on Embedded GPUs

GPUs have been employed in the embedded systems to handle increased amount of computation and satisfy the timing requirement. Therefore, the lifetime of embedded GPUs is considered one of the most important aspects to ensure functional correctness over a long period of time. Moreover, existing state-of-The-Art compiler-based GPU aging management techniques suffer from a considerable amount of performance overhead. In this paper, we propose a low-overhead aging-Aware resource management technique. The proposed technique extends the behavior of the existing warp scheduler and the instruction dispatcher to cluster the computational cores and distribute instructions based on the aging information. Compared to when using the original applications, our technique improves the aging of the embedded GPU by 30% on average. In addition, compared to the state-of-The-Art GPU aging management technique, our technique reduces the performance overhead by 16.4% on average while improving the aging by 3% on average.