Experimental verification and benchmark of in-memory principal component analysis by crosspoint arrays of resistive switching memory

In-memory computing (IMC) is gaining momentum as the most promising candidate for the upcoming non-von-Neumann, machine learning-optimized computing paradigm. Its intrinsic parallelism is well-suited to accelerate matrix-vector multiplications (MVM), which prove challenging for traditional architectures and are a fundamental operation in principal component analysis (PCA), one of the most renowned algorithms for data classification. Here, we show an experimental demonstration of a novel, IMC-based PCA algorithm by in-memory power iteration and deflation executed in a 4-kbit array of resistive random-access memory (RRAM). Our algorithm achieves 95.25% classification accuracy on the Wisconsin Diagnostic Breast Cancer dataset, matching closely results of a floating-point machine while providing a $250\times$ improvement in energy efficiency.