| 書目名稱 | Optimization of Spiking Neural Networks for Radar Applications | | 編輯 | Muhammad Arsalan | | 視頻video | http://file.papertrans.cn/704/703294/703294.mp4 | | 圖書封面 |  | | 描述 | .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‘ capabilit | | 出版日期 | Book 2024 | | 關(guān)鍵詞 | Air writing; Energy Efficiency; Big Data; Artificial Intelligence; FMCW Radar; Edge Devices; Privacy Prese | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-658-45318-3 | | isbn_softcover | 978-3-658-45317-6 | | isbn_ebook | 978-3-658-45318-3 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies |
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