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With storage and computation happening at the same place, computing inresistive crossbars minimizes data movement and avoids the memory bottleneckissue. It leads to ultra-high energy efficiency for data-intensiveapplications. However, defects in crossbars severely affect computing accuracy.Existing solutions, including re-training with defects and redundant designs,but they have limitations in practical implementations. In this work, weintroduce row shuffling and output compensation to mitigate defects withoutre-training or redundant resistive crossbars. We also analyzed the couplingeffects of defects and circuit parasitics. Moreover, We study differentcombinations of methods to achieve the best trade-off between cost andperformance. Our proposed methods could rescue up to 10% of defects inResNet-20 application without performance degradation.

Defects Mitigation in Resistive Crossbars for Analog Vector Matrix Multiplication