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portada Optimization of Spiking Neural Networks for Radar Applications
Type
Physical Book
Publisher
Author
Language
English
Pages
260
Format
Paperback
ISBN13
9783658453176
Edition No.
2024th

Optimization of Spiking Neural Networks for Radar Applications

Muhammad Arsalan (Author) · Springer Vieweg · Paperback

Optimization of Spiking Neural Networks for Radar Applications - Muhammad Arsalan

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Synopsis "Optimization of Spiking Neural Networks for Radar Applications"

This book offers a comprehensive exploration of the transformative role that edge devices play in advancing Internet of Things (IoT) applications. By providing real-time processing, reduced latency, increased efficiency, improved security, and scalability, edge devices are at the forefront of enabling IoT growth and success. As the adoption of AI on the edge continues to surge, the demand for real-time data processing is escalating, driving innovation in AI and fostering the development of cutting-edge applications and use cases. Delving into the intricacies of traditional deep neural network (deepNet) approaches, the book addresses concerns about their energy efficiency during inference, particularly for edge devices. The energy consumption of deepNets, largely attributed to Multiply-accumulate (MAC) operations between layers, is scrutinized. Researchers are actively working on reducing energy consumption through strategies such as tiny networks, pruning approaches, and weight quantization. Additionally, the book sheds light on the challenges posed by the physical size of AI accelerators for edge devices. The central focus of the book is an in-depth examination of SNNs' capabilities in radar data processing, featuring the development of optimized algorithms.

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