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This book is dedicated to neuromorphic computing. This is a volume that unravels the secrets of brain-inspired computational paradigms.
Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of neuromorphic systems, empowering both experts and newcomers to embrace the limitless potential of brain-inspired computing. Discover the fundamental principles that underpin neural computation as we journey through the origins of neuromorphic architectures, meticulously crafted to mimic the brain's intricate neural networks. Unlock the true essence of learning mechanisms - unsupervised, supervised, and reinforcement learning - and witness how these innovations are shaping the future of artificial intelligence.
Contents
1. End-to-End Benchmarking of Chiplet-Based In-Memory Computing
2. Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing
3. Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model
4. Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design
5. Neuromorphic Computing between Reality and Future Needs
6. A Study of the Comparison between Artificial Neural Networks, Logistic Regression and Similarity Weighted Instance-Based Learning in Modeling and Predicting Trends in Deforestation
7. Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing
8. Cortical Columns Computing Systems: Microarchitecture Model, Functional Building Blocks, and Design Tools
9. Artificial Intelligence Approaches for Studying the pp Interactions at High Energy Using Adaptive Neuro-Fuzzy Interface System
10. Study of Approaches to Predict Personality Using Digital Twin
11. Scaling Subspace-Driven Approaches Using Information Fusion
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