Dr. Huyen Do Profile

Dr. Huyen Do

at Amazon

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 31 May 2022 Presentation + Paper

Leveraging tensor methods in neural architecture search for the automatic development of lightweight convolutional neural networks

Mayur Dhanaraj, Huyen Do, Dinesh Nair, Cong Xu

Proceedings Volume 12097, 1209703 (2022) https://doi.org/10.1117/12.2621236

KEYWORDS: Convolution, Convolutional neural networks, Image classification, Network architectures

Read Abstract +

Most state-of-the-art Convolutional Neural Networks (CNNs) are bulky and cannot be deployed on resourceconstrained edge devices. In order to leverage the exceptional generalizability of CNNs on edge-devices, they need to be made efficient in terms of memory usage, model size, and power consumption, while maintaining acceptable performance. Neural architecture search (NAS) is a recent approach for developing efficient, edgedeployable CNNs. On the other hand, CNNs used for classification, albeit developed using NAS, often contain large fully-connected (FC) layers with thousands of parameters, contributing to the bulkiness of CNNs. Recent works have shown that FC layers can be compressed, with minimal loss in performance, if any, using tensor processing methods. In this work, for the first time in literature, we leverage tensor methods in the NAS framework to discover efficient CNNs. Specifically, we employ tensor contraction layers (TCLs) to compress fully connected layers in the NAS framework and control the trade-off between compressibility and classification performance by handcrafting the ranks of TCLs. Additionally, we modify the NAS procedure to incorporate automatic TCL rank search in an end-to-end fashion, without human intervention. Our numerical studies on a wide variety of datasets including CIFAR-10, CIFAR-100, and Imagenette (a subset of ImageNet) demonstrate the superior performance of the proposed method in the automatic discovery of CNNs, whose model sizes are manyfold smaller than other cutting-edge mobile CNNs, while maintaining similar classification performance.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years