Tag: Spiking

Free Download Spiking Neural P Systems: Theory, Applications and Implementations by Gexiang Zhang , Sergey Verlan , Tingfang Wu , Francis George C. Cabarle , Jie Xue , David Orellana-Martín , Jianping Dong , Luis Valencia-Cabrera , Mario J. Pérez-Jiménez
English | PDF EPUB (True) | 2024 | 435 Pages | ISBN : 9819792819 | 54.1 MB
Spiking neural P systems represent a significant advancement in the field of membrane computing, drawing inspiration from the communication patterns observed in neurons. Since their inception in 2006, these distributed and parallel neural-like computing models have gained popularity and emerged as important tools within the membrane computing area. As a key branch of the third generation of artificial neural networks, a fascinating research area of artificial intelligence, spiking neural P systems offer a captivating blend of theoretical elegance and practical utility. Their efficiency, Turing completeness, and real-life application characteristics, including interpretability and suitability for large-scale problems, have positioned them at the forefront of contemporary research in membrane computing and artificial intelligence.

Free Download Optimization of Spiking Neural Networks for Radar Applications by Muhammad Arsalan
English | September 2, 2024 | ISBN: 3658453176 | 260 pages | MOBI | 18 Mb
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.

Free Download Advanced Spiking Neural P Systems: Models and Applications
English | 2024 | ISBN: 9819752795 | 311 Pages | PDF EPUB (True) | 57 MB
In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems.

Free Download Optimization of Spiking Neural Networks for Radar Applications by Muhammad Arsalan
English | PDF EPUB (True) | 2024 | 253 Pages | ISBN : 3658453176 | 35.1 MB
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.