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Unit outline_

ELEC3608: Computer Architecture

Semester 2, 2021 [Normal day] - Remote

This unit of study explores the design of a computer system at the architectural and digital logic level. Topics covered include instruction sets, computer arithmetic, performance evaluation, datapath design, pipelining, memory hierarchies including caches and virtual memory, I/O devices, and bus-based I/O systems. Students will design a pipelined reduced instruction set processor.

Unit details and rules

Academic unit School of Electrical and Computer Engineering
Credit points 6
Prerequisites
? 
ELEC2602
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

ELEC3607. Knowledge of microprocessor systems (embedded systems architecture, design methodology, interfacing and programming) is required.

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Philip Heng Wai Leong, philip.leong@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam Final exam
Practical design of a CPU
60% Formal exam period 48 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Skills-based evaluation group assignment Lab 1 - ALU
1.25% Week 02 2 hours
Outcomes assessed: LO1 LO7 LO6 LO2
Skills-based evaluation group assignment Lab 2 - Simple CPU
1.25% Week 03 2 hours
Outcomes assessed: LO1 LO7 LO4 LO3 LO2
Skills-based evaluation group assignment Lab 3 - Register file
1.25% Week 04 2 hours
Outcomes assessed: LO1 LO7 LO4 LO3 LO2
Skills-based evaluation group assignment Lab 4 - Store Information
1.25% Week 05 2 hours
Outcomes assessed: LO1 LO4 LO3 LO2
Skills-based evaluation group assignment Lab 5 - Loops
1.25% Week 06 2 hours
Outcomes assessed: LO1 LO7 LO4 LO3 LO2
Skills-based evaluation group assignment Lab 6 - Assembly language
1.25% Week 07 2 hours
Outcomes assessed: LO1 LO7 LO4 LO3
Skills-based evaluation group assignment Lab7 - Caches
1.25% Week 09 2 hours
Outcomes assessed: LO1 LO7 LO6 LO4 LO3 LO2
Assignment group assignment Design exercise
30% Week 10 n/a
Outcomes assessed: LO1 LO2 LO3 LO5 LO6 LO7
Skills-based evaluation group assignment Lab8 - Matrix Multiplication
1.25% Week 12 2 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
group assignment = group assignment ?
Type E final exam = Type E final exam ?

Assessment summary

  • Labs: cover basic theory for course, prelabs must be completed prior to lab session.
  • Assignment: design project.
  • Final exam: closed book.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Performance Lecture (5 hr) LO1 LO4 LO6
Week 02 Instruction set architecture Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO6
Week 03 Hardware description languages Lecture and tutorial (5 hr) LO1 LO4
Week 04 Single cycle processor Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 05 Single cycle processor (ctd) Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 06 Pipelining Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 07 Pipelining (ctd) Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 08 Memory systems Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 09 Memory hierarchy Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 10 Virtual memory Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 11 Case study Lecture and tutorial (5 hr) LO1 LO4 LO6
Week 12 Case study (ctd) Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO6
Week 13 Summary Lecture and tutorial (5 hr) LO1 LO6

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • David Patterson and John Hennessy, Computer Organization and Design. Morgan Kaufmann.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand the literature in computer architecture design
  • LO2. Develop communication skills through the assignment
  • LO3. Work in teams through assignments and deal with project managements issues of completing a design exercise
  • LO4. Understand how economic issues affect computer designers
  • LO5. Design a pipelined RISC processor with memory hierarchy
  • LO6. Critically evaluate different pipelining schemes, memory designs and instruction sets
  • LO7. Model and benchmark the performance of different computer architectures.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Minor tidying up of inconsistencies

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.