research-article

Lightweight prediction based big/little design for efficient neural network inference

Authors:

Ye Tian,

Min Li,

Qiang XuAuthors Info & Claims

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Page 356

https://doi.org/10.1145/3318216.3363457

Published: 07 November 2019 Publication History

Get Access

Abstract

The accuracy improvement of neural networks may be only a few percentage points while the computation effort explodes, as previous less computation-intensive designs can already produce correct results for a large portion of inputs. We employ a lightweight prediction based Big/Little design to process those "easy" inputs with a little DNN and those "difficult" inputs with a big DNN.

Reference

[1]

E. Park, D. Kim, S. Kim, Y. Kim, G. Kim, S. Yoon, and S. Yoo. Big/little deep neural network for ultra low power inference. In Proceedings of the 10th International Conference on Hardware/Software Codesign and System Synthesis, pages 124--132. IEEE Press, 2015.

Digital Library

Google Scholar

Recommendations

Big/little deep neural network for ultra low power inference
CODES '15: Proceedings of the 10th International Conference on Hardware/Software Codesign and System Synthesis

Deep neural networks (DNNs) have recently proved their effectiveness in complex data analyses such as object/speech recognition. As their applications are being expanded to mobile devices, their energy efficiencies are becoming critical. In this paper, ...
Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of ...
Sunspot series prediction using a Multiscale Recurrent Neural Network
ISSPIT '10: Proceedings of the The 10th IEEE International Symposium on Signal Processing and Information Technology

A prediction scheme for sunspot series using a Multiscale Bilinear Recurrent Neural Network (M-BRNN) is proposed in this paper. The recurrent neural network adopted in this scheme is the Bilinear recurrent neural network. The M-BRNN is a combination of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

November 2019

455 pages

ISBN:9781450367332

DOI:10.1145/3318216

General Chairs:
Songqing Chen
George Mason University
,
Ryokichi Onishi
Toyota
,
Program Chairs:
Ganesh Ananthanarayanan
Microsoft Research
,
Qun Li
College of William & Mary

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

IEEE-CS\DATC: IEEE Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

SEC '19

Sponsor:

SIGMOBILE

SEC '19: The Fourth ACM/IEEE Symposium on Edge Computing

November 7 - 9, 2019

Virginia, Arlington

Acceptance Rates

SEC '19 Paper Acceptance Rate 20 of 59 submissions, 34%;

Overall Acceptance Rate 40 of 100 submissions, 40%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
157
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)2

Reflects downloads up to 10 Dec 2024

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Recommendations

Big/little deep neural network for ultra low power inference

Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

Sunspot series prediction using a Multiscale Recurrent Neural Network