Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

IEEE International Conference on Image Processing

Publication Version

Accepted Manuscript

Link to Published Version

http://dx.doi.org/10.1109/ICIP42928.2021.9506777

Publication Date

2021

Journal or Book Title

IEEE International Conference on Image Processing

First Page

1989

Last Page

1993

DOI

10.1109/ICIP42928.2021.9506777

Conference Title

IEEE International Conference on Image Processing

Conference Date

September 19-22, 2021

City

Anchorage, AK, USA

Abstract

Manually architecting Deep Neural Networks (DNNs) has led to the success of Deep Learning in many domains. However, recent DNNs designed using Neural Architecture Search (NAS) have exceeded manually designed architectures and have significantly reduced the human effort to develop complex networks. Current works use NAS to identify a cell architecture constrained by a fixed order of operations that is then replicated throughout the network. The constraints potentially limit the effectiveness of NAS in converging on a more efficient DNN architecture. In the first part of our paper, we propose “Operation Search,” a search on an enlarged topological space for U-net and its variants that retain efficiency. The idea is to allow for custom cells (operations and their sequence) at various levels of the network to maximize image quality while being sensitive to computation cost. In the second part of our paper, we propose custom quantization at various levels resulting in a mixed-precision network. Additionally, we increase the search efficiency by constraining the search space to use the same precision for both weights and activations at any level. This does not result in computational inefficiency because it matches the operand precisions supported by Tensor Core enabled GPUs.

Comments

This is a manuscript of an article published as Chitty-Venkata, Krishna Teja, Arun K. Somani, and Sreenivas Kothandaraman. "Searching Architecture and Precision for U-net based Image Restoration Tasks." In 2021 IEEE International Conference on Image Processing (ICIP), pp. 1989-1993. IEEE, 2021. DOI: 10.1109/ICIP42928.2021.9506777. Posted with permission.

Rights

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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