Thinking Machines - Neural Networks and Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks. It is a recommended book for those who have basic knowledge of machine learning as well as those who want to learn more about the current trends of machine learning in general. Presents a clear understanding of various available machine learning hardware accelerator solutions, which can be applied to selected machine learning algorithmsBridges the research areas of nonlinear control problems, model prediction and diagnostics problems, where artificial intelligence and machine learning is usedPresents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning models, as well as what are the limiting factors to design and product productionSurveys current trends and models in neuromorphic computing and neural network hardware architecturesApplies machine learning, neural networking, and AI to real world problems, using case studies and multiple examples INDICE: 1. Introduction 2. Legacy Architecture 3. Machine Learning and Implementation Methods 4. Machine Learning Hardware 5. Performance Improvement Methods 6. Development of Machine Learning Models 7. Case Study of Hardware Implementation 8. Key Points of Hardware Implementation 9. Conclusion Appendix A. Deep Learning Basics B. Advanced Network Models C. Country-Specific Research and Development Trends D. Impact on Society
- ISBN: 978-0-12-818279-6
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 200
- Fecha Publicación: 01/12/2020
- Nº Volúmenes: 1
- Idioma: Inglés