Phase change materials-based photonic computing

• Verfasst von: Bhaskaran, Harish [VerfasserIn]
• Titel: Phase change materials-based photonic computing
• Mitwirkende: Pernice, Wolfram [MitwirkendeR]
• Verf.angabe: edited by Harish Bhaskaran, Wolfram Pernice
• Ausgabe: 1st ed.
• Verlagsort: San Diego
• Verlag: Elsevier
• Jahr: 2024
• Umfang: 1 online resource (272 pages)
• Gesamttitel/Reihe: Materials Today Series
• Fussnoten: Description based on publisher supplied metadata and other sources
• ISBN: 978-0-12-823492-1
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Phase change materials-based photonic computing. - Amsterdam : Elsevier, 2024. - X, 260 Seiten
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• K10plus-PPN: 1879861569

Abstract

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.