摘要
This paper proposes built-in self-repair (BISR) strategies for computing-in-memory (CIM) architectures to mitigate clipping errors in convolutional neural networks (CNNs) and enhance overall CIM performance.First, we propose a BISR circuit that detects and compensates clipping errors in real-time. When a bitline output of the CIM approaches the upper limit of the analog-to-digital converter (ADC), a pre-stored compensation value is added to preserve inference accuracy. Second, we observe a strong correlation between clipping errors and the sum-of-weight-bits on bitlines. Based on this, we introduce a grouping scheme that records compensation values by sum-of-weight-bits ranges, reducing storage overhead while maintaining accuracy.Experiments on image classification show the proposed BISR recovers the top-1 accuracy loss to approximately 1% and improves CIM throughput by up to 3.2×. The hardware incurs only about 5% additional area relative to the CIM macro, excluding ADCs and DACs.