Embedded deep learning : algorithms, architectures and circuits for always-on neural network processing /
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| Author / Creator: | Moons, Bert, author. |
|---|---|
| Imprint: | Cham : Springer, 2018. ©2019 |
| Description: | 1 online resource (xvi, 206 pages) |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11746434 |
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| 245 | 1 | 0 | |a Embedded deep learning : |b algorithms, architectures and circuits for always-on neural network processing / |c Bert Moons, Daniel Bankman, Marian Verhelst. |
| 264 | 1 | |a Cham : |b Springer, |c 2018. | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (xvi, 206 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 29, 2018) | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 0 | |a Education |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85041001 | |
| 650 | 0 | |a Learning, Psychology of. |0 http://id.loc.gov/authorities/subjects/sh85075526 | |
| 650 | 0 | |a Motivation in education. |0 http://id.loc.gov/authorities/subjects/sh85087566 | |
| 650 | 7 | |a EDUCATION |x Essays. |2 bisacsh | |
| 650 | 7 | |a EDUCATION |x Organizations & Institutions. |2 bisacsh | |
| 650 | 7 | |a EDUCATION |x Reference. |2 bisacsh | |
| 650 | 7 | |a Imaging systems & technology. |2 bicssc | |
| 650 | 7 | |a Electronics engineering. |2 bicssc | |
| 650 | 7 | |a Circuits & components. |2 bicssc | |
| 650 | 7 | |a Education |x Data processing. |2 fast |0 (OCoLC)fst00902579 | |
| 650 | 7 | |a Learning, Psychology of. |2 fast |0 (OCoLC)fst00995009 | |
| 650 | 7 | |a Motivation in education. |2 fast |0 (OCoLC)fst01027541 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Bankman, Daniel, |e author. | |
| 700 | 1 | |a Verhelst, Marian, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Moons, Bert. |t Embedded deep learning. |d Cham : Springer, 2018 |z 3319992228 |z 9783319992228 |w (OCoLC)1044854003 |
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