Accelerating Machine Learning-Based Memristor Compact Modeling Using Sparse Gaussian Process

Research on dedicated circuits for multiply and accumulate processing, which is vital to machine learning (ML), using memristors has attracted considerable attention. However, memristors have unknown operating principles, making it challenging to create compact models with sufficient accuracy. This study proposes a compact modeling method based on Gaussian process for memristors. Although various ML-based modeling methods have been proposed, only the reproduction accuracy has been evaluated using SPICE circuit simulator, and long learning times have not been sufficiently discussed. The proposed method reduces the learning and inference times using a Gaussian process with considering sparsity. An evaluation using data from memristor devices obtained by actual measurements demonstrates that the proposed method achieves over 2,629 times faster than conventional method using long short-term memory (LSTM). Moreover, inference on a commercial SPICE simulator can be performed with the same accuracy and computation time. All experimental environments, including the source code, are available at https://github.com/sntnmchr/SGPR-memristor/blob/main/README.md.