  Resource balancing has been effectively used to mitigate the long-term aging
effects of Negative Bias Temperature Instability (NBTI) in multi-core and
Graphics Processing Unit (GPU) architectures. In this work, we investigate this
strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel
application-to-CGRA allocation approach. By introducing important extensions to
the reconfiguration logic and the datapath, we enable the dynamic movement of
configurations throughout the fabric and allow overutilized Functional Units
(FUs) to recover from stress-induced NBTI aging. Implementing the approach in a
resource-constrained state-of-the-art CGRA reveals $2.2\times$ lifetime
improvement with negligible performance overheads and less than $10\%$ increase
in area.
