Co-Designing NVM-based Systems for Machine Learning and In-memory Search Applications

With the rapid development of the Internet of Things, machine learning applications on edge devices with limited resources face challenges due to large data scales and irregular memory access patterns. Non-volatile memory (NVM) technologies provide promising solutions by offering larger capacity, low leakage power, and data persistence. In this paper, we discuss the potential of NVM technology in enhancing machine learning applications by improving energy efficiency and reducing latency through in-memory computation and different NVM write modes. The insights from this analysis provide valuable guidance to device researchers and system architects working to develop highperformance systems for machine learning and accelerators in large-scale search applications using NVMs.

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Publication Details

Published
2024-10-27
DOI
https://doi.org/10.1145/3676536.3697114
Citations
1
Primary Topic
Ferroelectric and Negative Capacitance Devices
Type
article
Field-Weighted Citation Impact
0.21

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Co-Designing NVM-based Systems for Machine Learning and In-memory Search Applications

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Co-Designing NVM-based Systems for Machine Learning and In-memory Search Applications

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article en
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Abstract

With the rapid development of the Internet of Things, machine learning applications on edge devices with limited resources face challenges due to large data scales and irregular memory access patterns. Non-volatile memory (NVM) technologies provide promising solutions by offering larger capacity, low leakage power, and data persistence. In this paper, we discuss the potential of NVM technology in enhancing machine learning applications by improving energy efficiency and reducing latency through in-memory computation and different NVM write modes. The insights from this analysis provide valuable guidance to device researchers and system architects working to develop highperformance systems for machine learning and accelerators in large-scale search applications using NVMs.

Karlsruhe Institute of Technology (DE), University of Notre Dame (US), National Taiwan University (TW), TU Dortmund University (DE), Karlsruhe University of Education (DE), University of Twente (NL)
Deutsche Forschungsgemeinschaft, National Science and Technology Council
Openalex Percentile: Top 43%
Ferroelectric and Negative Capacitance Devices
0.21
DOIOpen Access PDFOpenAlex
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