Edge Computing
| Title | Συστήματα Επεξεργασίας στο Άκρο του Δικτύου / Edge Computing |
| Code | IHST211 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Georgios Keramidas |
| Common | No |
| Status | Active |
| Course ID | 600020721 |
Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) MF
Registered students: 1
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 7.5 |
Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) PF
Registered students: 4
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 7.5 |
| Academic Year | 2021 – 2022 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600200641
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600200641
- eLearning: https://elearning.auth.gr/course/view.php?id=16333
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge in computing architecture and operating systems.
Learning Outcomes
Students that successfully complete the course will be able to:
- Explain key challenges related to edge computing devices
- Explain key challenges related to computational intensive algorithms in resourse limited devices
- Design reliable and fault-tolerant systems
- Apply software and hardware level low power techniques
- Design real-time systems and applications
- Explain key challenges in increasing the instruction level parallelism
- Apply techniques to increase the parallelization in applications
- Apply code level transformations for single core and multi-core applications
- Apply parallelization techniques at various levels for machine learning algorithms
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Characteristics of computing systems for the very-edge, edge, and cloud. Characteristics of computational and memory intensive algorithms (data analytics, machine learning, deep learning algorithms). Frameworks for analyzing computing requirements (simulators, profiling techniques, cloud-based tools and methods). Design objectives for the edge.
Dependability and fault-tolerance: Definition of the terms reliability, availability, maintainability, security and performance. Errors, faults, and failures. Soft and hard errors. Fault models. Redundancy in space and time. Redundancy in software and hardware. Graceful degradation. Applications. Power aware computing: Dynamic and static power. Moore law and Dennard law. Low power processors and systems. Dynamic frequency and voltage scaling (DVFS). DVFS for IoT and edge-level processors. Approximate computing. Project in analyzing the power consumption in a (smartphones):power models based on performance counters.
Real-time responsiveness and worst-case execution time: Operating systems and processes in devices with limited resources. Performance estimation and memory management in ΙοΤ and edge-level devices. Project in using the yocto tool for building Linux kernels.
Edge processing solutions (the portfolio of ARM): Embedded processors. Embedded hardware systems. Basic princicples of increasing the instruction level parallelism (Superscalar, VLIW, SIMD, MIMD, dynamic execution, memory hierarchies, hyperthreading, SMT processors, multicores).
Code optimization techniques and single and multicore architectures. Overview of static and dynamic optimizations techniques. Code level optimizations: Instruction dependencies, loop level transformations, statics branch prediction, data-level transformations. Improving the locality of references and number of misses (caches, TLBs). Programming techniques for multiprocessors and multithreaded architectures.
Parallel architectures for machine learning kernels. Parallel architectures, parallel systems, accelerators, application specific processors for machine learning kernels. Parallelization of machine learning applications in general purpose processors and accelerators.
Keywords
Edge computing, Computional an memory requirements, Profiling, Reliability, Redundancy, Low power computing, Dynamic voltage and frequency scaling, Dynamic and static power, Real-time response, Worst-case execution time, Embedded processors, Code level transformations, Parallel computing for machine learning applications
Educational Material Types
- Notes
- Slide presentations
- Multimedia
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
Use of computer slides and specialized software for simulating single core and multicore systems. Use of Course Management System to provide learning material and support teacher-student communication.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | ✓ | ✓ | |
| Reading Assigment | 60 | ✓ | ✓ | ✓ | |
| Project | 83 | ✓ | ✓ | ✓ | |
| Written assigments | ✓ | ✓ | ✓ | ||
| Exams | 3 | ✓ | ✓ | ✓ | |
| Total | 185 |
Student Assessment
Description
Written assignments, Programming project
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Summative)
- Report (Formative, Summative)
Bibliography
Additional bibliography for study
1) Parallel Computer Organization and Design, Michel Dubois, Murali Annavaram, Per Stenstrom, 2012.
2) Reconfigurable computing, Scott Hauck, André DeHon, Morgan Kauffman.
3) Parallel Computing for Data Science: With Examples in R, C++ and CUDA, Norman Matloff, Chapman and Hall/CRC The R Series, 2016.
4) Scaling up machine-learning: Parallel and Distributed Approaches, Ron Bekkerman, Mikhail Bilenko, John Langford, Cambridge University Press, 2018.
Last Update
19-02-2022
