Energy-Efficient Devices and Circuits for Neuromorphic Computing

Herausgeber: Khanday, Farooq Ahmad

• Broschiertes Buch

Produktbeschreibung

Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to this field, covering topics from neuron dynamics to energy-efficient CMOS devices and circuits. The book delves into theoretical analysis of learning processes in spiking neural networks, two-terminal neuromorphic devices, material-engineered neuromorphic devices, and novel biomimetic Si devices. It offers insights into the latest developments in non-volatile memory crossbar arrays and emerging post-CMOS devices. Overall, it provides a comprehensive overview of energy-efficient neuromorphic computing architecture. This book is an essential resource for researchers, engineers, and students working in neuromorphic computing and energy-efficient electronics.

Produktdetails

• Verlag: Elsevier - Health Sciences Division
• Seitenzahl: 540
• Erscheinungstermin: 6. Oktober 2025
• Englisch
• Abmessung: 235mm x 191mm
• Gewicht: 450g
• ISBN-13: 9780443299810
• ISBN-10: 0443299811
• Artikelnr.: 73603320

Inhaltsangabe

1. Biological neural systems NEW
2. Fundamentals of neuron dynamics and Neural Networks NEW
3. Foundations, recent developments and applications of spiking neural
networks (SNNs)
4. Training and learning processes of SNNs
5. Introduction to Neuromorphic Computing
6. The Need for Energy Efficiency in Neuromorphic Computing v Review of
Neuromorphic devices and Circuits
7. Energy-efficient devices for Neuromorphic computing
8. Novel biomimetic devices for energy efficient synapses and neurons OLD
9. Analog and Digital CMOS circuits for Energy Efficient Neuromorphic
Computing
10. Energy-efficient Neuromorphic computing systems with emerging post-CMOS
devices
11. Energy Efficient Neuromorphic Computing Architectures and Processing
12. Nonvolatile memory crossbar arrays for energy efficient neuromorphic
computing
13. Energy Efficient Neuromorphic Vision Systems
14. Neuromorphic sensors and in-sensor computing
15. Practical Applications of Energy-Efficient Neuromorphic Computing
16. Current and future challenges of Neuromorphic Computing