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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Ueno Shuichi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8(H117) Fri7-8(H117)
- Group
- -
- Course number
- ICT.A515
- Credits
- 2
- Academic year
- 2017
- Offered quarter
- 4Q
- Syllabus updated
- 2017/9/22
- Lecture notes updated
- 2017/10/10
- Language used
- Japanese
- Access Index
-
Course description and aims
This course introduces the theory of parallel and VLSI computation such as models of parallel computation, parallel algorithms and architectures, area-time complexity for VLSI, and quantum computation.
Student learning outcomes
At the end of this course, students will be able to:
1) Explain models and computational complexity of parallel computation.
2) design and analize parallel algorithms.
3) explain the principle of quantum computation.
Keywords
model of parallel computation, parallel algorithm, computational complexity, VLSI, Boolean circuit, reversible circuit, quantum circuit
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Students should review the topics covered in each class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction | Students must make sure they understand what significance the course holds for them by chechking their learning portfolio. |
| Class 2 | RAM Computation | Review of serial algorithms and computational complexity |
| Class 3 | PRAM model | Understand the PRAM model of parallel computation |
| Class 4 | Sorting on PRAM | Analysis of parallel sorting algorithms on PRAMs |
| Class 5 | Parallel Computational Complexity | Understand the complexity of parallel computation |
| Class 6 | Network Model | Understand the network model of parallel computation |
| Class 7 | Sorting on Arrays | Analysis of parallel sorting algorithms on arrays |
| Class 8 | Parallel Computation on Hypercubes | Analysis of parallel sorting algorithms on hypercubes |
| Class 9 | VLSI Layout | Understand the VLSI layouts |
| Class 10 | VLSI Computational Complexity | Understand the area-time complexity of VLSI |
| Class 11 | Boolean Circuit Complexity | Understand the complexity of Boolean circuits |
| Class 12 | Physics of Computation | Understand physics of computation |
| Class 13 | Reversible Circuits | Understand reversible circuits |
| Class 14 | Quantum Circuits | Understand quantum circuits |
| Class 15 | Quantum Computation | Analysis of quantum algorithms |
Textbook(s)
None required.
Reference books, course materials, etc.
Course materials are provided during class.
Assessment criteria and methods
Students will be assessed on their understanding of the models and computational complexity of parallel computation, the design and analysis of parallel algorithms, and the principle of quantum computation.
Students' course scores are based on their reports.
Related courses
- Discrete Structures and Algorithms
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites are necessary, but enrollment in the course of discrete structures and algorithms or equivalent is desirable.
