Video Converter
Audio Converter
Image Converter
PDF Converter
File Compressor
cdeeparch a compact deep neural network architecture for

cDeepArch: A Compact Deep Neural Network Architecture for Mobile - PowerPoint PPT Presentation

Sep 16, 2022 •197 likes •439 views

cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing Kang Yang 1 , Xiaoqing Gong 1 , Yang Liu 2 , Zhenjiang Li 2 , Tianzhang Xing 1 , Xiaojiang Chen 1 , Dingyi Fang 1 1 Northwest University, China 2 City University of Hong Kong

  1. cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing Kang Yang 1 , Xiaoqing Gong 1 , Yang Liu 2 , Zhenjiang Li 2 , Tianzhang Xing 1 , Xiaojiang Chen 1 , Dingyi Fang 1 1 Northwest University, China 2 City University of Hong Kong 1

  2. Motivation … Camera + Learning Gyro. Technology Acc.

  3. Application ? Cognitive decline

  4. Application First-person view Cognitive aid system Recognizing pot cup open open close

  5. Common design principle . . . Rich sensor data Recognized by learning Applications

  6. Challenges Large targets . . .

  7. Challenges • Deep Learning Too large Big deep neural network Resource-limited

  8. Challenges • Deep Learning inaccurate Original Shrunk No quantitative measure on model model available resource conditions

  9. Any countermeasure? 0101… Server • Long and uncontrollable latency • High Service cost Potential privacy leakage •

  10. Our solution Context (office) Large targets . . .

  11. Our solution compact network (Office) Context recognition + Object recognition Context-oriented (computer, large and deep network target recognition mouse…) compact network adequate storage computation resource

  12. Our solution energy Context recognition computation + Available resource conditions Context-oriented target recognition • not based on designer’s experience • Formulation facilitated configuration

  13. Convolutional Neural Network Image data Conv1 Pool1 Conv2 Pool2 FC1 • Convolutional layer (dominant) • Pooling layer • Full connected layer

  14. Formulation facilitated configuration Selected W F W o *W o S P C (W+2P)*(W+2P)

  15. From computation to resource cost Conv1:64 fc:5 Conv2:128 computation resource(energy) designed network Conv1:16 Conv2:32 fc:5 : computation : actual resource consumption a small scale network

  16. Now… Context recognition + Object recognition Context-oriented target recognition Recognition task decomposition • • Formulation facilitated configuration From formulation to estimate the resource consumption •

  17. Enhancement: Convolutional layer Original Conv1 Conv2 Conv3 model Separated Conv1a Conv1b Conv2 Conv3 model #$ % − ⁄ 1 ) ⁄ ! ≤ ) 2

  20. Evaluation

  21. Experiments setup Dataset: • o Context recognition : § MIT Place2 (related to the daily contexts ) Object recognition : o § Cifar10 § Cifar100 (20 classes associated contexts)

  22. Evaluation results • Overall performance 10 targets 20 targets

  23. Conclusion 1, 2, 3 1. Large targets Decompose recognition task 2. Systematic way to configure network Execution overhead formulation facilitated configuration 3. Enhancement techniques

Recommend

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural

Neural Information Retrieval Wassila Lalouani 1 Plan Neural network architectures Neural IR tasks Neural IR architecture Feature Representations Neural IR query auto completion Neural IR query suggestion Neural IR document

1.48k views • 18 slides
Interstate Medical Licensure Compact Overview Define Need for compact Compacts in

Interstate Medical Licensure Compact Overview Define Need for compact Compacts in

Interstate Medical Licensure Compact Overview Define Need for compact Compacts in Idaho Key Principles of the Compact Eligibility for compact licensure Compact Commission Benefits for Idaho Next Steps What is an

722 views • 24 slides
Compact Subsets Theorem Suppose that K is a subset of a topological space X. 1 If X is compact

Compact Subsets Theorem Suppose that K is a subset of a topological space X. 1 If X is compact

Compact Subsets Theorem Suppose that K is a subset of a topological space X. 1 If X is compact and K is closed, then K is compact. 2 If X is Hausdorff and K is compact, then K is closed. Theorem Suppose that X is Hausdorff, that K is a compact

192 views • 3 slides
Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and

Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and

Deep Neural Networks and Deep Reinforcement Learning Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and Courville [chapt. 6,7,8]; AIMA [sect. 21.1-21.3]; Sutton and Barto, Reinforcement Learning: an

530 views • 35 slides
Agenda Overview of deep learning Building a FAQ model with DeepLearning4J Integrating

Agenda Overview of deep learning Building a FAQ model with DeepLearning4J Integrating

Agenda Overview of deep learning Building a FAQ model with DeepLearning4J Integrating with a chatbot application Overview of deep learning AI ML Deep Learning Neural network architecture Neural network architecture Neural

473 views • 46 slides
A Neural Network Architecture for Detec2ng Gramma2cal Errors in SMT A Neural Network Architecture

A Neural Network Architecture for Detec2ng Gramma2cal Errors in SMT A Neural Network Architecture

A Neural Network Architecture for Detec2ng Gramma2cal Errors in SMT A Neural Network Architecture for Detec2ng Gramma2cal Errors in SMT Morpho-Syntac-c features outperform word embeddings on this task Syntac-c n-grams improve the

1.18k views • 38 slides
Neural Networks and Handwriting Recognition Background Neural Networks Neural Network Steven

Neural Networks and Handwriting Recognition Background Neural Networks Neural Network Steven

Neural Networks and Handwriting Recognition Steven Sloss Math 164 Neural Networks and Handwriting Recognition Background Neural Networks Neural Network Steven Sloss Structure Training Neural Networks Math 164 Motivation Problem

895 views • 41 slides
Neural Networks Neural networks arise from attempts to model Neural Networks human/animal

Neural Networks Neural networks arise from attempts to model Neural Networks human/animal

Feed-forward Networks Network Training Error Backpropagation Deep Learning Feed-forward Networks Network Training Error Backpropagation Deep Learning Neural Networks Neural networks arise from attempts to model Neural Networks

382 views • 9 slides
The Fundamentals of Deep Learning Building Blocks Theory with Applications Neural Units Neural

The Fundamentals of Deep Learning Building Blocks Theory with Applications Neural Units Neural

Deep Learning Antecedents Vision Case Study The Fundamentals of Deep Learning Building Blocks Theory with Applications Neural Units Neural Nets Deep Neural Nets Application ConvNets Jon Krohn LSTMs untapt jon@untapt.com

1.08k views • 106 slides
Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan Zelener Machine Learning Reading Group January 7 th 2016 The Graduate Center, CUNY Objectives Explain some of the trends of deep learning and

684 views • 44 slides
Neural Architecture Search Yu Cao What is Neural Architecture Search (NAS) Selecting the optimal

Neural Architecture Search Yu Cao What is Neural Architecture Search (NAS) Selecting the optimal

Neural Architecture Search Yu Cao What is Neural Architecture Search (NAS) Selecting the optimal network architecture automatically via machine instead of design it manually. It is an important aspect of AutoML. NAS search space 1.

3.44k views • 37 slides
Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics Activation Functions Stochastic Gradient Descent (SGD) Regularization (Dropout) Training Tips and Tricks Neural Network (NN)

830 views • 45 slides
Learning Neural Networks Learning Neural Networks Neural Networks can represent complex Neural

Learning Neural Networks Learning Neural Networks Neural Networks can represent complex Neural

Learning Neural Networks Learning Neural Networks Neural Networks can represent complex Neural Networks can represent complex decision boundaries decision boundaries Variable size. Any boolean function can be Variable size. Any boolean

361 views • 14 slides
PLAYING ATARI WITH DEEP REINFORCEMENT LEARNING NEURAL NETWORK VISION FOR ROBOT DRIVING ARJUN

PLAYING ATARI WITH DEEP REINFORCEMENT LEARNING NEURAL NETWORK VISION FOR ROBOT DRIVING ARJUN

PLAYING ATARI WITH DEEP REINFORCEMENT LEARNING NEURAL NETWORK VISION FOR ROBOT DRIVING ARJUN CHANDRASEKARAN DEEP LEARNING AND PERCEPTION (ECE 6504) PLAYING ATARI WITH DEEP REINFORCEMENT LEARNING NEURAL NETWORK VISION FOR ROBOT DRIVING

1.08k views • 62 slides
AGN deep multiwavelength AGN deep multiwavelength AGN deep multiwavelength surveys: surveys:

AGN deep multiwavelength AGN deep multiwavelength AGN deep multiwavelength surveys: surveys:

AGN deep multiwavelength AGN deep multiwavelength AGN deep multiwavelength surveys: surveys: surveys: the case of the Chandra Deep Field South the case of the Chandra Deep Field South the case of the Chandra Deep Field South Fabrizio Fiore

428 views • 21 slides
Introduction to Artificial Intelligence Neural Networks - Deep Learning for NLP Janyl Jumadinova

Introduction to Artificial Intelligence Neural Networks - Deep Learning for NLP Janyl Jumadinova

Introduction to Artificial Intelligence Neural Networks - Deep Learning for NLP Janyl Jumadinova November 21, 2016 Neural Networks 2/20 Neural Networks 3/20 Neural Networks Neural computing requires a number of neurons , to be connected

815 views • 21 slides
Dominant Dimension and Orders over Cohen-Macaulay Rings 2. This Year 1. Last Year 1 Table of

Dominant Dimension and Orders over Cohen-Macaulay Rings 2. This Year 1. Last Year 1 Table of

Maurice Auslander Distinguished Lectures and International Conference zgr Esentepe April 25, 2019 University of Toronto Dominant Dimension and Orders over Cohen-Macaulay Rings 2. This Year 1. Last Year 1 Table of contents Last Year

736 views • 17 slides
Speech recognition in the presence of highly non-stationary noise based on spatial, spectral and

Speech recognition in the presence of highly non-stationary noise based on spatial, spectral and

Speech recognition in the presence of highly non-stationary noise based on spatial, spectral and temporal speech/ noise modeling combined with dynamic variance adaptation M. Delcroix, K. Kinoshita, T. Nakatani, S. Araki, A. Ogawa, T.

426 views • 26 slides
The The W World Oce ld Ocean an Re Regime: Polluted Wa Waters and Exhausted Fi Fishe

The The W World Oce ld Ocean an Re Regime: Polluted Wa Waters and Exhausted Fi Fishe

The The W World Oce ld Ocean an Re Regime: Polluted Wa Waters and Exhausted Fi Fishe heries Peter J. Jacques, Professor School of Politics, Security, and International Affairs & The National Center for Integrated Coastal Research

284 views • 16 slides
GPSCP: A General-Purpose Support-Circuit Preconditioning Approach to Large Scale SPICE Accurate

GPSCP: A General-Purpose Support-Circuit Preconditioning Approach to Large Scale SPICE Accurate

Design Automation Group GPSCP: A General-Purpose Support-Circuit Preconditioning Approach to Large Scale SPICE Accurate Preconditioning Approach to Large-Scale SPICE-Accurate Nonlinear Circuit Simulations Authors: Xueqian Zhao Xueqian Zhao

391 views • 25 slides
Germany: The Dominant Power in Europe The Dominant Power in Europe meelis_kitsing@uml.edu

Germany: The Dominant Power in Europe The Dominant Power in Europe meelis_kitsing@uml.edu

Germany: The Dominant Power in Europe The Dominant Power in Europe meelis_kitsing@uml.edu Source: Palmer 2004 Source: Palmer 2004 Population: 82,431,390 (July 2005 est.) Life expectancy at birth: 78.65 years (total population) 75.66 years

694 views • 55 slides
Tractable Term Structure ModelsA New Approach Bruno Feunou, Jean-S ebastien Fontaine, Anh

Tractable Term Structure ModelsA New Approach Bruno Feunou, Jean-S ebastien Fontaine, Anh

Tractable Term Structure ModelsA New Approach Bruno Feunou, Jean-S ebastien Fontaine, Anh Le, Christian Lundblad Bank of Canada and FRBSF Fixed Income Conference November 2015 Feunou, Fontaine, Le Tractable Term Structure Modeling: A New

650 views • 37 slides
Investment Funds and the Corporate-Bond Market in the Covid-19 Crisis Itay Goldstein Wharton

Investment Funds and the Corporate-Bond Market in the Covid-19 Crisis Itay Goldstein Wharton

Investment Funds and the Corporate-Bond Market in the Covid-19 Crisis Itay Goldstein Wharton School, University of Pennsylvania Hutchins Center, Brookings Institution May 27, 2020 Itay Goldstein: Corporate-Bond Market in the Covid-19 Crisis

290 views • 12 slides
Vi Viden en: A Attack cker er Id Iden entifi fication on on on In In-V -Veh ehicl cle

Vi Viden en: A Attack cker er Id Iden entifi fication on on on In In-V -Veh ehicl cle

Vi Viden en: A Attack cker er Id Iden entifi fication on on on In In-V -Veh ehicl cle Ne e Networ orks Kyong-Tak Cho and Kang G. Shin Univ Univer ersity o sity of Mic f Michig higan, A an, Ann A nn Arbo rbor r CCS 2017

254 views • 24 slides

More recommend

Explore More Topics

Stay informed with curated content and fresh updates.

animals pets art culture automotive transportation business finance computer internet construction architecture education-career electronics communication

Logo Sambuz

Unleash a World of Digital Possibilities—Browse, Share, and Explore Content Without Boundaries

Useful Links

About Us Privacy Services FAQ's Contact

Newsletter

Stay Informed & Inspired—Subscribe for the Latest Updates, Tips, and Exclusive Content Directly to Your Inbox.

Mail Us

mail@sambuz.com

2026 SAMBUZ, All right reserved.