Spatial pattern prediction based management of faulty data caches

Technology scaling leads to significant faulty bit rates in on-chip caches. In this work, we propose a methodology to mitigate the impact of defective bits (due to permanent faults) in first-level set-associative data caches. Our technique assumes that faulty caches are enhanced with the ability of disabling their defective parts at cache subblock granularity. Our experimental findings reveal that while the occurrence of hard-errors in faulty caches may have a significant impact in performance, a lot of room for improvement exists, if someone is able to take into account the spatial reuse patterns of the to-be-referenced blocks (not all the data fetched into the cache is accessed). To this end, we propose frugal PC-indexed spatial predictors (with very small storage requirements) to orchestrate the (re)placement decisions among the fully and partially unusable faulty blocks. Using cycle-accurate simulations, a wide range of scientific applications, and a plethora of cache fault maps, we showcase that our approach is able to offer significant benefits in cache performance.