Optimization Of Spiking Neural Networks For Radar Applications
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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.
Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems
This book provides novel theoretical foundations and experimental demonstrations of Spiking Neural Networks (SNNs) in tasks such as radar gesture recognition for IoT devices and autonomous drone navigation using a fusion of retina-inspired event-based camera and radar sensing. The authors describe important new findings about the Spike-Timing-Dependent Plasticity (STDP) learning rule, which is widely believed to be one of the key learning mechanisms taking place in the brain. Readers will be enabled to create novel classes of edge AI and robotics applications, using highly energy- and area-efficient SNNs
Neural Computing for Advanced Applications
This book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024. The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections: Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Computer vision, and their engineering applications. Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks. Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications; Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security.