A Case for Memory Content-Based Detection and Mitigation of Data-Dependent Failures in DRAM

DRAM cells in close proximity can fail depending on the data content in neighboring cells. These failures are called data-dependent failures. Detecting and mitigating these failures online while the system is running in the field enables optimizations that improve reliability, latency, and energy efficiency of the system. All these optimizations depend on accurately detecting every possible data-dependent failure that could occur with any content in DRAM. Unfortunately, detecting all data-dependent failures requires the knowledge of DRAM internals specific to each DRAM chip. As internal DRAM architecture is not exposed to the system, detecting data-dependent failures at the system-level is a major challenge. Our goal in this work is to decouple the detection and mitigation of data-dependent failures from physical DRAM organization such that it is possible to detect failures without knowledge of DRAM internals. To this end, we propose MEMCON, a memory content-based detection and mitigation mechanism for data-dependent failures in DRAM. MEMCON does not detect every possible data-dependent failure. Instead, it detects and mitigates failures that occur with the current content in memory while the programs are running in the system. Using experimental data from real machines, we demonstrate that MEMCON is an effective and low-overhead system-level detection and mitigation technique for data-dependent failures in DRAM.