Exploiting Application Behaviors for Resilient Static Random Access Memory Arrays in the Near-Threshold Computing Regime

Near-Threshold Computing embodies an intriguing choice for mobile processors due to the promise of superior energy efficiency, extending the battery life of these devices while reducing the peak power draw. However, process, voltage, and temperature variations cause a significantly high failure rate of Level One cache cells in the near-threshold regime a stark contrast to designs in the super-threshold regime, where fault sites are rare. This thesis work shows that faulty cells in the near-threshold regime are highly clustered in certain regions of the cache. In addition, popular mobile benchmarks are studied to investigate the impact of run-time workloads on timing faults manifestation. A technique to mitigate the run-time faults is proposed. This scheme maps frequently used data to healthy cache regions by exploiting the application cache behaviors. The results show up to 78% gain in performance over two other state-of-the-art techniques

Resilient Cache Design for Mobile Processors in the Near-Threshold Regime

Near-threshold computing embodies an intriguing choice for extending mobile processor battery life due to its high energy efficiency. However, process, voltage and temperature variations cause a significantly high failure rate of level 1 cache SRAM cells in the near-threshold regime compared to the super-threshold regime. In this work, we show that faulty cells in the near-threshold regime are highly clustered in certain regions of the cache. We then propose a low overhead technique to dynamically reduce the performance penalty due to process variations by exploiting the spatial congregation of faulty cells and application cache behaviors. Our experimental results demonstrate up to 78% reduction in performance loss over two state-of-art techniques