Adaptive Testing of Compute-in-Memory Based CNNs Using Probabilistic Test Acceptance Limits
Anurup Saha, Kwondo Ma, Chandramouli Amarnath, Moinuddin K. Qureshi, Abhijit Chatterjee · 2025
Compute-in-memory (CiM) based convolutional neural network (CNN) accelerators achieve low-power inference, utilizing memristive crossbar arrays for matrix multiplications. However, inherent conductance variations within the crossbar introduce computational errors. These errors propagate to the CNN output and cause image misclassification, leading to substantial accuracy degradation. This paper addresses the critical challenge of efficient and reliable post-manufacture testing for CiM-based CNN accelerators. We propose a novel test image sampling methodology, which iteratively applies sampled images from the CNN's testing dataset using progressive random sampling (PRS) to a device under test (DUT) and estimates a confidence interval for the DUT accuracy. Based on the confidence interval and the acceptable accuracy threshold, the test labels a DUT as “pass” or “fail”. Furthermore, if we have access to an initial set of DUTs, we apply the images from the CNN's testing dataset to these DUTs and leverage the DUT outputs to rank-order test images. We develop a sequential estimation test (SET) framework, where the images from the CNN's testing dataset are sequentially applied according to a predetermined rank and the test terminates when a DUT can be confidently labeled as “pass” or “fail” based on the applied images. In each case, the number of applied test images adapts to the quality of the DUT. Experiments show that PRS and SET achieve$2.2\times$and$4.6\times$speedup compared to state-of-the-art test methodologies.