Robust Embedded Intelligence on Cellular Neural Networks
Published on 31. July 2019
Book
Hardback
350 pages
978-87-7022-100-9 (ISBN)
Description
Machine Intelligence (MI) is the motor that drives the modern information-rich society. Supercomputing and parallel coding have made MI programs feasible and the theoretical base of MI has matured. Graphical Processors (GPs) have found new life as carrier for low-cost supercomputing. The road towards lower cost & size leads to embedding systems, such as self-driving cars and intelligent houses.
Machine intelligence has not only opened many disruptive venues but given simultaneously a lot of anxiety. Compared to the many accidents in the early days of automotive traffic, the few problems with the TESLA cars are already sufficient for major concern. Evidently, still research is required to bring MI on the appropriate safety levels that rule the type tests for acceptance on the European automotive market.
In the ACM Turing Award 2018, Hennessey and Patterson prophesize the breakthrough of Domain-Specific Processor. A typical example is the modern vision hardware in the automotive domain. Such applications bring collectively self-driving in reach for safety concerns. It features a domain-specific mix of hardware and software. Hardware can be correctly designed & manufactured and will not change afterwards. Software can be formally proven but efficiency requires to keep it close to the platform.
Robust Embedded Intelligence on Cellular Neural Networks makes the reader familiar with the mathematical and electronic techniques to turn a data-driven problem into a safe embedded solution. In particular, it treats aspects on Cellular Neural Networks (CNN) for reliable visual recognition in a wide range of practical applications, highlighting vein feature extraction and license plate recognition.
Machine intelligence has not only opened many disruptive venues but given simultaneously a lot of anxiety. Compared to the many accidents in the early days of automotive traffic, the few problems with the TESLA cars are already sufficient for major concern. Evidently, still research is required to bring MI on the appropriate safety levels that rule the type tests for acceptance on the European automotive market.
In the ACM Turing Award 2018, Hennessey and Patterson prophesize the breakthrough of Domain-Specific Processor. A typical example is the modern vision hardware in the automotive domain. Such applications bring collectively self-driving in reach for safety concerns. It features a domain-specific mix of hardware and software. Hardware can be correctly designed & manufactured and will not change afterwards. Software can be formally proven but efficiency requires to keep it close to the platform.
Robust Embedded Intelligence on Cellular Neural Networks makes the reader familiar with the mathematical and electronic techniques to turn a data-driven problem into a safe embedded solution. In particular, it treats aspects on Cellular Neural Networks (CNN) for reliable visual recognition in a wide range of practical applications, highlighting vein feature extraction and license plate recognition.
More details
Series
Language
English
Place of publication
Gistrup
Denmark
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-87-7022-100-9 (9788770221009)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Lambert Spaanenburg, Comoray AB, Sweden and Lund University, Sweden
Suleyman Malki, Comoray AB, Sweden
Suleyman Malki, Comoray AB, Sweden
Content
Module 1: Cellular Neural Networks
1. Introduction
2. The Concept
3. Discrete-Time CNN
Module 2: Template Design
4. Simple Morphological Functions
5. Complex Morphological Functions
6. Templates for Systems
Module 3: Hardware Implementations
7. State-of-the-Art
8. Unrolling CNN on FPGA
9. Stretching the Communication
Module 4: From Networks to Systems
10. Memory Considerations
11. Vein Feature Extractions
12. Applications
Module 5: Embedding CNN Systems
13. Template Optimization
14. System Architecture
15. Further Considerations
1. Introduction
2. The Concept
3. Discrete-Time CNN
Module 2: Template Design
4. Simple Morphological Functions
5. Complex Morphological Functions
6. Templates for Systems
Module 3: Hardware Implementations
7. State-of-the-Art
8. Unrolling CNN on FPGA
9. Stretching the Communication
Module 4: From Networks to Systems
10. Memory Considerations
11. Vein Feature Extractions
12. Applications
Module 5: Embedding CNN Systems
13. Template Optimization
14. System Architecture
15. Further Considerations