
Thinking Machines
Machine Learning and Its Hardware Implementation
Description
Key Features
- 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
Readership
Table of Contents
1. Introduction
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
9. ConclusionAppendix
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
Product details
- No. of pages: 322
- Language: English
- Copyright: © Academic Press 2021
- Published: March 27, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128182796
- eBook ISBN: 9780128182802