Roadmap on emerging hardware and technology for machine learning
Name
Berggren_2021_Nanotechnology_32_012002.pdf
Description
Published version
Size
6.58 MB
Format
Unknown
Checksum (MD5)
5ef229497957ed63a2cba14ab9f2c326
Author(s)
Berggren, Karl K
Lin, Peng
Rupp, Jennifer Lilia Marguerite
Roques-Carmes, Charles
Date Issued
2021
Journal
Nanotechnology
Publisher
IOP Publishing
Version
Final published version
Abstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1361-6528/aba70f
