Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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 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.
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
Graduate students and researchers in computer science/computer engineering, neural engineering, model prediction
2. Traditional Microarchitectures
3. Machine Learning and its Implementation
4. Applications, ASICs, and Domain-Specific Architectures
5. Machine Learning Model Development
6. Performance Improvement Methods
7. Study of Hardware Implementation
8. Keys of Hardware Implementation
A. Basics of Deep Learning
B. Modeling of Deep Learning Hardware
C. Advanced Network Models
D. National Trends for Research and Its Investment
E. Machine Learning and Social
- No. of pages:
- © Academic Press 2021
- 1st March 2021
- Academic Press
- Paperback ISBN:
Shigeyuki Takano developed digital signal processors at Sanyo Semiconductor. At Dowango, he prototyped a hardware video transcoder, and discovered the power of deep learning; he read deep learning books and found that deep learning tasks can be implemented as hardware. He is currently an engineer a leading automotove company where he worked on the development of an in-house deep learning processor. He is currently pursuing a PhD in Computer Engineering at Keio University, Kanagawa. His interests include processor architecture, especially for domain-specific architectures.
Keio University, Kanagawa, Japan
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.