Mitigating the impact of faults in unreliable memories for error-resilient applications

Shrikanth Ganapathy, Georgios Karakonstantis, Adam Teman, Andreas Burg
2015 Proceedings of the 52nd Annual Design Automation Conference on - DAC '15  
Inherently error-resilient applications in areas such as signal processing, machine learning and data analytics provide opportunities for relaxing reliability requirements, and thereby reducing the overhead incurred by conventional error correction schemes. In this paper, we exploit the tolerable imprecision of such applications by designing an energyefficient fault-mitigation scheme for unreliable data memories to meet target yield. The proposed approach uses a bit-shuffling mechanism to
more » ... e faults into bit locations with lower significance. This skews the bit-error distribution towards the low order bits, substantially limiting the output error magnitude. By controlling the granularity of the shuffling, the proposed technique enables trading-off quality for power, area, and timing overhead. Compared to errorcorrection codes, this can reduce the overhead by as much as 83% in read power, 77% in read access time, and 89% in area, when applied to various data mining applications in 28 nm process technology.
doi:10.1145/2744769.2744871 dblp:conf/dac/GanapathyKTB15 fatcat:mwwngmb4pvfvhoh7bpynbncy6a