Thinking Machines Machine Learning and Its Hardware Implementation - Shigeyuki Takano
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Thinking Machines: Machine Learning and Its 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 le ... Pilns apraksts
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Aprašymas
This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning.
- Presents a clear understanding of various available machine learning hardware accelerator solutions that can be applied to selected machine learning algorithms
- Offers key insights into the development of hardware, from algorithms, software, logic circuits, to hardware accelerators
- Introduces the baseline characteristics of deep neural network models that should be treated by hardware as well
- Presents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning models
- Surveys current trends and models in neuromorphic computing and neural network hardware architectures
- Outlines the strategy for advanced hardware development through the example of deep learning accelerators
Vairāk informācijas
| Autors | Shigeyuki Takano |
|---|---|
| Izdevējs | Elsevier Science |
| Izlaides gads | 2021 |
| Vāka tips | Mīkstais vāks |
| EAN | 9780128182796 |