• search hit 1 of 10
Back to Result List

In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory

  • 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.

Export metadata

Additional Services

Search Google Scholar
Metadaten
Author: Piergiulio Mannocci, Andrea Baroni, Enrico Melacarne, Cristian Zambelli, Piero Olivo, Eduardo Perez, Christian WengerORCiD, Daniele Ielmini
DOI:https://doi.org/10.1109/MNANO.2022.3141515
ISSN:1932-4510
Title of the source (English):IEEE Nanotechnology Magazine
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2022
Tag:Multilevel switching; RRAM; neural network
Volume/Year:16
Issue number:2
First Page:4
Last Page:13
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.