Defects Mitigation in Resistive Crossbars for Analog Vector Matrix Multiplication
This addresses accuracy issues in energy-efficient computing hardware for data-intensive applications, but it is incremental as it builds on existing defect mitigation approaches.
The paper tackles the problem of defects in resistive crossbars, which reduce computing accuracy for analog vector matrix multiplication, by introducing row shuffling and output compensation methods, achieving up to 10% defect mitigation in ResNet-20 without performance loss.
With storage and computation happening at the same place, computing in resistive crossbars minimizes data movement and avoids the memory bottleneck issue. It leads to ultra-high energy efficiency for data-intensive applications. 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, we introduce row shuffling and output compensation to mitigate defects without re-training or redundant resistive crossbars. We also analyzed the coupling effects of defects and circuit parasitics. Moreover, We study different combinations of methods to achieve the best trade-off between cost and performance. Our proposed methods could rescue up to 10% of defects in ResNet-20 application without performance degradation.