Coverart for item
The Resource Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing, Bert Moons, Daniel Bankman, Marian Verhelst

Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing, Bert Moons, Daniel Bankman, Marian Verhelst

Label
Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing
Title
Embedded deep learning
Title remainder
algorithms, architectures and circuits for always-on neural network processing
Statement of responsibility
Bert Moons, Daniel Bankman, Marian Verhelst
Creator
Contributor
Author
Subject
Language
eng
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Moons, Bert
Dewey number
370.15/23
Index
index present
LC call number
LB1065
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Bankman, Daniel
  • Verhelst, Marian
http://library.link/vocab/subjectName
  • Education
  • Learning, Psychology of
  • Motivation in education
Label
Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing, Bert Moons, Daniel Bankman, Marian Verhelst
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Acknowledgments; Contents; Acronyms; 1 Embedded Deep Neural Networks; 1.1 Introduction; 1.2 Machine Learning; 1.2.1 Tasks, T; 1.2.2 Performance Measures, P; 1.2.3 Experience, E; 1.2.3.1 Supervised Learning; 1.2.3.2 Unsupervised Learning; 1.3 Deep Learning; 1.3.1 Deep Feed-Forward Neural Networks; 1.3.2 Convolutional Neural Networks; 1.3.3 Recurrent Neural Networks; 1.3.4 Training Deep Neural Networks; 1.3.4.1 Loss Functions; 1.3.4.2 Backpropagation; 1.3.4.3 Optimization; 1.3.4.4 Data Sets; 1.3.4.5 Regularization; 1.3.4.6 Training Frameworks
  • 1.4 Challenges for Embedded Deep Neural Networks1.5 Book Contributions; References; 2 Optimized Hierarchical Cascaded Processing; 2.1 Introduction; 2.2 Hierarchical Cascaded Systems; 2.2.1 Generalizing Two-Stage Wake-Up Systems; 2.2.2 Hierarchical Cost, Precision, and Recall; 2.2.3 A Roofline Model for Hierarchical Classifiers; 2.2.4 Optimized Hierarchical Cascaded Sensing; 2.3 General Proof of Concept; 2.3.1 System Description; 2.3.2 Input Statistics; 2.3.3 Experiments; 2.3.3.1 Optimal Number of Stages; 2.3.3.2 Optimal Stage Metrics in a Hierarchy; 2.3.4 Conclusion
  • 2.4 Case study: Hierarchical, CNN-Based Face Recognition2.4.1 A Face Recognition Hierarchy; 2.4.2 Hierarchical Cost, Precision, and Recall; 2.4.3 An Optimized Face Recognition Hierarchy; 2.5 Conclusion; References; 3 Hardware-Algorithm Co-optimizations; 3.1 An Introduction to Hardware-Algorithm Co-optimization; 3.1.1 Exploiting Network Structure; 3.1.2 Enhancing and Exploiting Sparsity; 3.1.3 Enhancing and Exploiting Fault-Tolerance; 3.2 Energy Gains in Low-Precision Neural Networks; 3.2.1 Energy Consumption of Off-Chip Memory-Access; 3.2.2 Generic Hardware Platform Modeling
  • 3.3 Test-Time Fixed-Point Neural Networks3.3.1 Analysis and Experiments; 3.3.2 Influence of Quantization on Classification Accuracy; 3.3.2.1 Uniform Quantization and Per-Layer Rescaling; 3.3.2.2 Per-Layer Quantization; 3.3.3 Energy in Sparse FPNNs; 3.3.4 Results; 3.3.5 Discussion; 3.4 Train-Time Quantized Neural Networks; 3.4.1 Training QNNs; 3.4.1.1 Train-Time Quantized Weights; 3.4.1.2 Train-Time Quantized Activations; 3.4.1.3 QNN Input Layers; 3.4.1.4 Quantized Training; 3.4.2 Energy in QNNs; 3.4.3 Experiments; 3.4.3.1 Benchmarks; 3.4.3.2 QNN Topologies; 3.4.4 Results; 3.4.5 Discussion
  • 3.5 Clustered Neural Networks3.6 Conclusion; References; 4 Circuit Techniques for Approximate Computing; 4.1 Introducing the Approximate Computing Paradigm; 4.2 Approximate Computing Techniques; 4.2.1 Resilience Identification and Quality Management; 4.2.2 Approximate Circuits; 4.2.3 Approximate Architectures; 4.2.4 Approximate Software; 4.2.5 Discussion; 4.3 DVAFS: Dynamic-Voltage-Accuracy-Frequency-Scaling; 4.3.1 DVAFS Basics; 4.3.1.1 Introducing the DVAFS Energy-Accuracy Trade-Off; 4.3.1.2 Precision Scaling in DVAFS; 4.3.2 Resilience Identification for DVAFS; 4.3.3 Energy Gains in DVAFS
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319992228
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319992235
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1059124864
  • (OCoLC)1059124864
Label
Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing, Bert Moons, Daniel Bankman, Marian Verhelst
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Acknowledgments; Contents; Acronyms; 1 Embedded Deep Neural Networks; 1.1 Introduction; 1.2 Machine Learning; 1.2.1 Tasks, T; 1.2.2 Performance Measures, P; 1.2.3 Experience, E; 1.2.3.1 Supervised Learning; 1.2.3.2 Unsupervised Learning; 1.3 Deep Learning; 1.3.1 Deep Feed-Forward Neural Networks; 1.3.2 Convolutional Neural Networks; 1.3.3 Recurrent Neural Networks; 1.3.4 Training Deep Neural Networks; 1.3.4.1 Loss Functions; 1.3.4.2 Backpropagation; 1.3.4.3 Optimization; 1.3.4.4 Data Sets; 1.3.4.5 Regularization; 1.3.4.6 Training Frameworks
  • 1.4 Challenges for Embedded Deep Neural Networks1.5 Book Contributions; References; 2 Optimized Hierarchical Cascaded Processing; 2.1 Introduction; 2.2 Hierarchical Cascaded Systems; 2.2.1 Generalizing Two-Stage Wake-Up Systems; 2.2.2 Hierarchical Cost, Precision, and Recall; 2.2.3 A Roofline Model for Hierarchical Classifiers; 2.2.4 Optimized Hierarchical Cascaded Sensing; 2.3 General Proof of Concept; 2.3.1 System Description; 2.3.2 Input Statistics; 2.3.3 Experiments; 2.3.3.1 Optimal Number of Stages; 2.3.3.2 Optimal Stage Metrics in a Hierarchy; 2.3.4 Conclusion
  • 2.4 Case study: Hierarchical, CNN-Based Face Recognition2.4.1 A Face Recognition Hierarchy; 2.4.2 Hierarchical Cost, Precision, and Recall; 2.4.3 An Optimized Face Recognition Hierarchy; 2.5 Conclusion; References; 3 Hardware-Algorithm Co-optimizations; 3.1 An Introduction to Hardware-Algorithm Co-optimization; 3.1.1 Exploiting Network Structure; 3.1.2 Enhancing and Exploiting Sparsity; 3.1.3 Enhancing and Exploiting Fault-Tolerance; 3.2 Energy Gains in Low-Precision Neural Networks; 3.2.1 Energy Consumption of Off-Chip Memory-Access; 3.2.2 Generic Hardware Platform Modeling
  • 3.3 Test-Time Fixed-Point Neural Networks3.3.1 Analysis and Experiments; 3.3.2 Influence of Quantization on Classification Accuracy; 3.3.2.1 Uniform Quantization and Per-Layer Rescaling; 3.3.2.2 Per-Layer Quantization; 3.3.3 Energy in Sparse FPNNs; 3.3.4 Results; 3.3.5 Discussion; 3.4 Train-Time Quantized Neural Networks; 3.4.1 Training QNNs; 3.4.1.1 Train-Time Quantized Weights; 3.4.1.2 Train-Time Quantized Activations; 3.4.1.3 QNN Input Layers; 3.4.1.4 Quantized Training; 3.4.2 Energy in QNNs; 3.4.3 Experiments; 3.4.3.1 Benchmarks; 3.4.3.2 QNN Topologies; 3.4.4 Results; 3.4.5 Discussion
  • 3.5 Clustered Neural Networks3.6 Conclusion; References; 4 Circuit Techniques for Approximate Computing; 4.1 Introducing the Approximate Computing Paradigm; 4.2 Approximate Computing Techniques; 4.2.1 Resilience Identification and Quality Management; 4.2.2 Approximate Circuits; 4.2.3 Approximate Architectures; 4.2.4 Approximate Software; 4.2.5 Discussion; 4.3 DVAFS: Dynamic-Voltage-Accuracy-Frequency-Scaling; 4.3.1 DVAFS Basics; 4.3.1.1 Introducing the DVAFS Energy-Accuracy Trade-Off; 4.3.1.2 Precision Scaling in DVAFS; 4.3.2 Resilience Identification for DVAFS; 4.3.3 Energy Gains in DVAFS
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319992228
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319992235
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1059124864
  • (OCoLC)1059124864

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