Abstract
The record-breaking success of convolutional neural networks (CNNs) comes at the cost of a large amount of model parameters. The resulting prohibitive memory storage and data movement energy have been limiting the extensive deployment of deep learning on daily-life edge devices which usually have limited storage capability and are battery-powered. To this end, we explore the employment of a recently published weight clustering technique, called deep k-Means which makes use of the redundancy within CNN parameters for reduced memory storage and data movement, and demonstrate k-Means's effectiveness in the context of an interactive real-time object detection using three representative daily-life devices (iPhone, iPad and FPGA).
Original language | English (US) |
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Title of host publication | 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728103976 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Event | 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan Duration: May 26 2019 → May 29 2019 |
Publication series
Name | Proceedings - IEEE International Symposium on Circuits and Systems |
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Volume | 2019-May |
ISSN (Print) | 0271-4310 |
Conference
Conference | 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 |
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Country/Territory | Japan |
City | Sapporo |
Period | 5/26/19 → 5/29/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE