Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Advanced Computer Architecture | |||
Reference | EN4541 | Version | 6 |
Created | June 2017 | SCQF Level | SCQF 10 |
Approved | March 2004 | SCQF Points | 15 |
Amended | September 2017 | ECTS Points | 7.5 |
Aims of Module | |||
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To enable the student to study the architecture of high performance computer systems and to examine alternative architectures for computer systems designed to meet specific requirements. |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Analyse and critically assess the design of advanced computer systems. |
2 | Design key features of advanced processing and memory systems. |
3 | Develop solutions to computing problems using computational intelligence based architectures. |
4 | Implement and test solutions of computational intelligence based architectures using computer simulation. |
Indicative Module Content |
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Advanced techniques for processor design: pipelined and superpipelined processors, branch prediction, very long instruction word (VLIW) processors, superscalar processors. Vector processors: architecture and instruction sets, vector length and stride, vector chaining. Multiprocessors: parallel processor classification, topologies. Supercomputer architecture: clusters, highly parallel systems, cloud computing, graphic processors. Memory: interleaved, bank phased. Cache memory: associative, mapping techniques, multi-level caches, support for multiprocessors (snooping, MESI). Floating point processing. Digital signal processors: architecture and features, applications, multiprocessor support. Multicore processors, multithreading, storage systems. Alternative architectures: artificial neural networks (biological basis, perceptron, back-propagation, feedback), fuzzy logic (fuzzification, inference engine, defuzzification), evolutionary artificial neural networks, other current methods. |
Module Delivery |
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The module is taught using a structured programme of lectures, tutorials and student-centred learning. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | 39 | 39 |
Non-Contact Hours | 111 | 111 |
Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
TOTAL | 150 | 150 |
Actual Placement hours for professional, statutory or regulatory body |   |   |
ASSESSMENT PLAN | |||||
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If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
Component 1 | |||||
Type: | Coursework | Weighting: | 30% | Outcomes Assessed: | 4 |
Description: | Component 1 is a coursework which involves the development of software related to computational intelligence based architectures. | ||||
Component 2 | |||||
Type: | Examination | Weighting: | 70% | Outcomes Assessed: | 1, 2, 3 |
Description: | Component 1 a closed book examination. |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
To pass the module, you must achieve at least a 40% weighted average mark in the exam and coursework. In addition you need to achieve at least 35% in both the individual exam and coursework Components. | |
Module Grade | Minimum Requirements to achieve Module Grade: |
A | =>70% |
B | 60-69% |
C | 50-59% |
D | 40-49% |
E | 35-39% |
F | 0-34% |
NS | Non-submission of work by published deadline or non-attendance for examination |
Module Requirements | |
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Prerequisites for Module | Microprocessors and Microcontrollers (EN2540) or equivalent. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | J.L. HENNESSY and D.A. PATTERSON, Computer Architecture: A Quantitative Approach, 5th ed. San Francisco: Morgan Kaufmann, 2011. |
2 | K. L. Du and M. N. S. Swamy, Neural Networks in a Soft Computing Framework, Springer, 2006. |
3 | S. Haykin, Neural Networks and Learning Machines: A Comprehensive Foundation, Pearson, 3rd edition, 2008. |
4 | M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, 2nd edition, 2002. |