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Compact Functional Test Generation for Memristive Deep Learning Implementations using Approximate Gradient Ranking

Deep learning has been applied in many fields such as autonomous driving, medical imaging, and sensor data processing to solve complex computational problems. Hardware implementation of deep learning applications can be accelerated with Memristive devices such as Resistive Random-Access Memory (ReRAM), Phase Change Memories (PCM), and Spin Transfer Torque-Magnetic RAM (STT-MRAM) via utilizing compute-in-memory (CiM) architectures. They offer many benefits compared to conventional charge-based memories including non-volatility, zero leakage power, and low power consumption. However, memristors suffer from various manufacturing and runtime defects and variations due to their immature fabrication process, inherent device properties, and external environmental factors such as temperature. Those faults can negatively impact the performance and reliability of the implemented deep learning applications. Therefore, detection of such faults before they catastrophically affect the accuracy of deep learning applications is very important. In this paper, we proposed a simple and non-invasive functional test generation method with a black-box approach that samples a small subset of the training data as the test vectors based on their approximate gradient ranking. The proposed method requires only up to 0.128% of training data stored in hardware as test vectors and achieves test coverage up to 100% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 benchmark datasets.