Testing and Tuning of RRAM-Based DNNs: A Machine Learning-Assisted Approach

Resistive random access memory (RRAM) is highly attractive for use in deep neural networks (DNNs) due to its ability to perform matrix-vector multiplications with ultra-low power consumption, which is essential for implementing DNNs. However, testing and tuning RRAM-based DNNs is challenging due to the vulnerability of RRAM crossbars to manufacturing process variations. This difficulty is compounded by the complexity of modern DNNs and the large number of test stimuli (e.g., images) required to assess performance, such as classification accuracy. Post-manufacture tuning is further complicated by the need to test the DNN through each tuning iteration to ensure convergence to acceptable performance levels. This research proposes a machine learning-assisted alternative testing and tuning framework for DNNs that: (a) allows DNNs to be tested with a limited number of test stimuli (few test images) and (b) enables post-manufacture tuning of complex DNNs to be performed in tens of seconds, as opposed to hours or more. The core methodology, along with associated algorithms and test cases, is described.