TY - GEN A1 - Mannocci, Piergiulio A1 - Baroni, Andrea A1 - Melacarne, Enrico A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory T2 - IEEE Nanotechnology Magazine N2 - In Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrixvector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMCbased PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43%, close to floatingpoint implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing. KW - RRAM KW - Multilevel switching KW - neural network Y1 - 2022 U6 - https://doi.org/10.1109/MNANO.2022.3141515 SN - 1932-4510 VL - 16 IS - 2 SP - 4 EP - 13 ER -