Hardware for machine learning: Challenges and opportunities
Name
Sze_Hardware for.pdf
Size
2.93 MB
Format
Adobe PDF
Checksum (MD5)
8cae3ba0616d075fb0c9f5c90db9186d
Author(s)
Sze, Vivienne
Chen, Yu-Hsin
Einer, Joel
Suleiman, Amr AbdulZahir
Zhang, Zhengdong
Date Issued
July 2017
Journal
2017 IEEE Custom Integrated Circuits Conference (CICC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sze, Vivienne, et al. "Hardware for Machine Learning: Challenges and Opportunities." Custom Integrated Circuits Conference (CICC), 30 April - 3 May, 2017, Austin, TX, IEEE, 2017, pp. 1–8.
Version
Author's final manuscript
Abstract
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CICC.2017.7993626
