Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers
Name
_2017__SSCS__Machine_Learning_Tutorial (1).pdf
Description
Accepted version
Size
2.64 MB
Format
Adobe PDF
Checksum (MD5)
aafb99d7dc7020a28432665d2a710b08
Author(s)
Sze, Vivienne
Date Issued
November 2017
Journal
IEEE Solid-State Circuits Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sze, Vivienne. "Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers." IEEE Solid-State Circuits Magazine 9, 4 (November 2017): 46-54 © 2017 IEEE
Version
Author's final manuscript
Abstract
Machine learning is becoming increasingly important in this era of big data. It enables us to extract meaningful information from the overwhelming amount of data being generated and collected every day. This information can be used to analyze and understand the data to identify trends (e.g., surveillance and portable/wearable electronics) or to take immediate action (e.g., robotics/drones, self-driving cars, and smart Internet of Things). In many applications, embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns or limitations in the communication bandwidth. However, sensor devices often have stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Circuit designers can play an important role in addressing these challenges by developing energy-efficient platforms to perform the necessary processing for machine learning. In this article, we will give a short overview of the key concepts in machine learning, discuss its challenges particularly in the embedded space, and highlight various opportunities where circuit designers can help to address these challenges.
MIT Department
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/mssc.2017.2745798
