Phase change materials-based photonic computing
Front Cover -- Phase Change Materials-Based Photonic Computing -- Copyright Page -- Contents -- List of contributors -- 1 Introduction to phase change photonics -- 1.1 Background -- 1.1.1 Phase change materials and computing -- 1.1.2 Optoelectronic applications of phase change materials -- 1.1.3 Pho...
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| Main Author: | |
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| Other Authors: | |
| Format: | Article (Journal) Book/Monograph |
| Language: | English |
| Published: |
San Diego
Elsevier
2024
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| Edition: | 1st ed. |
| Series: | Materials Today Series
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| Online Access: | Aggregator, lizenzpflichtig: https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=31075605
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| Author Notes: | edited by Harish Bhaskaran, Wolfram Pernice |
MARC
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| 520 | |a Front Cover -- Phase Change Materials-Based Photonic Computing -- Copyright Page -- Contents -- List of contributors -- 1 Introduction to phase change photonics -- 1.1 Background -- 1.1.1 Phase change materials and computing -- 1.1.2 Optoelectronic applications of phase change materials -- 1.1.3 Photonics-a primer -- 1.1.4 How this book is organized -- References -- 2 Non von Neumann computing concepts -- 2.1 Introduction -- 2.2 Essential advances -- 2.2.1 Many core architecture -- 2.2.2 Processing in memory -- 2.2.3 Nonvolatile memory -- 2.3 Phase change memory -- 2.3.1 Technology overview -- 2.3.2 Key computational properties -- 2.3.2.1 Multilevel memory states -- 2.3.2.2 Accumulative behavior -- 2.3.2.3 Nonvolatility of binary states -- 2.3.3 Nonidealities -- 2.4 Computational paradigms -- 2.4.1 Deep learning: a use case of multistate property -- 2.4.2 Neuromorphic engineering: a use case of accumulative property -- 2.4.3 Hyperdimensional computing: a use case of nonvolatile binary states property -- 2.5 Outlook -- References -- 3 Photonic computing: an introduction -- 3.1 Introduction -- 3.2 Why photonics in computing -- 3.2.1 Bandwidth -- 3.2.2 Speed -- 3.2.3 Latency -- 3.2.4 Energy efficiency -- 3.3 Photonic neural networks -- 3.3.1 Neuron models -- 3.4 Toward scalable neural networks -- 3.5 Synapse and its optical implementation -- 3.6 Interconnection protocols for matrix multiplication -- 3.6.1 Broadcast-and-weight -- 3.6.2 Coherent -- 3.6.3 Free-space diffractive network -- 3.7 Weight tuning mechanisms and devices -- 3.8 Other photonic computing models -- 3.8.1 Photonic reservoir computing -- 3.8.2 Photonic Ising machine -- 3.8.3 Photonic quantum computing -- 3.9 Challenges and current areas of progress -- 3.9.1 Photonic-electronic integration -- 3.9.2 New materials and devices -- 3.9.3 Algorithms -- References. | ||
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