Co-Designing NVM-based Systems for Machine Learning and In-memory Search Applications
Part Of
IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Start Page
1
End Page
8
ISSN
10923152
ISBN (of the container)
979-840071077-3
ISBN
[9798400710773]
Date Issued
2024-10-27
Author(s)
Henkel, Jörg
Siddhu, Lokesh
Nassar, Hassan
Bauer, Lars
Chen, Jian-Jia
Hakert, Christian
Seidl, Tristan
Chen, Kuan Hsun
Hu, Xiaobo Sharon
Li, Mengyuan
Wei, Ming-Liang
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 high-performance systems for machine learning and accelerators in large-scale search applications using NVMs.
Event(s)
43rd International Conference on Computer-Aided Design, ICCAD 2024
Publisher
ACM
Type
conference paper