PhD in Computer and Information Engineering

OVERVIEW

Overview

The PhD Program in Computer and Information Engineering (CIEN) is managed by the Department of CIEN at the College of Computing and Mathematical Sciences at Khalifa University. The program is designed to equip students with state-of-the-art knowledge in one of the fastest-growing fields in high-tech. The program is multidisciplinary with a focus on three main themes, which are Computing Systems, Optimization and Security, and Communications and Networks. The program is highly flexible and offers courses in various CIEN areas, allowing students to choose a set of courses that particularly suits their research and career plans. While some students prefer to focus on one of the three themes, other students choose to diversify and broaden their knowledge by studying courses from more than one theme.

The program is open to outstanding students from all nationalities and offers highly competitive scholarships. Moreover, the PhD program accepts talented students with only a BSc degree given that they meet the admission requirements.

The PhD in CIEN at Khalifa University is supported by leading research centers and Labs equipped with world-class researchers and facilities addressing global real-world challenges. The main centers that support the CIEN PhD program are, the 6G Research Center, Center for Cyber-Physical Systems, KU Center for Autonomous Robotic Systems, Embedded Systems & Imaging Lab, Advanced Digital Systems Design Lab, and SYSTEM-ON-CHIP LAB (SoCL).

Graduating with a PhD in CIEN opens doors to diverse career paths in academia, industry, and research. Academically, roles such as assistant professor and postdoctoral researcher are common, involving teaching, curriculum development, and leading research projects. In the industry, PhD holders are sought after for positions like research scientist, AI/ML engineer, data scientist, and senior software or hardware engineer, contributing to advancements in areas like artificial intelligence, machine learning, cybersecurity, and wireless communications. Additionally, opportunities exist in specialized fields like quantum computing, robotics, digital forensics, smart cities, digital twin technology, and IoT.

Criteria for Admission

Program Requirements

Students admitted to the PhD program with a Master鈥檚 Degree must satisfy the following requirements:

Complete a minimum of 24 credits of coursework (8 courses of 3 credit hours each). This is in addition to two zero-credit PhD Research Seminar courses. Subject to the advisor’s approval, up to two PhD level courses (maximum of 6 credits) can be taken from other doctoral programs offered at KU.
Pass all courses with a minimum passing grade of C in every course.
Achieve an overall CGPA (Cumulative Grade Point Average) of at least 3.0 out of 4.
Pass the Written Qualifying Exam (WQE), which assesses the technical background of the student. WQE is typically administered at the end of the 2nd regular semester after a full-time student鈥檚 admission into the PhD program, and before the end of the 4th semester in the case of a part-time student.
Pass the Research Proposal Examination (RPE), typically before the end of the 4th semester for full-time students and before the end of the 6th semester for part-time students.
Have at least one full paper accepted for publication in a quartile one ranked journal, per Scopus, in the research field of the dissertation before submitting the request of intent to defend the dissertation. The paper must be based on one of the research contributions in the dissertation, and the student must be the lead author of the paper.
Complete a Dissertation on original research and defend it successfully in a viva voce Dissertation Defense examination.

Students admitted to the PhD program with only a Bachelor鈥檚 Degree must satisfy the following requirements:

Complete a minimum of 36 credits of coursework (12 courses of 3 credit hours each). This is in addition to two zero-credit PhD Research Seminar courses. Subject to the advisor’s approval, up to two PhD level courses (maximum of 6 credits) can be taken from other doctoral programs offered at KU.
Pass all courses with a minimum passing grade of C in every course.
Achieve an overall CGPA (Cumulative Grade Point Average) of at least 3.0 out of 4.
Pass the Written Qualifying Exam (WQE), which assesses the technical background of the student. WQE is administered after the student successfully completes a minimum of 27 credits of formal coursework.
Pass the Research Proposal Examination (RPE), typically before the end of the 6th semester of full-time study.
Have at least one full paper accepted for publication in a quartile one ranked journal, per Scopus, in the research field of the dissertation before submitting the request of intent to defend the dissertation. The paper must be based on one of the research contributions in the dissertation, and the student must be the lead author of the paper.
Complete a Dissertation on original research and defend it successfully in a viva voce Dissertation Defense examination.

STRUCTURE & REQUIREMENTS

Category	Credit hours required
Program Core	3
Technical Electives	21
PhD Research Seminar I	0
PhD Research Seminar II	0
PhD Written Qualifying Exam	0
PhD Research Proposal Exam	0
PhD Research Dissertation	36
Total	60

Theme	Code	Course Title
Computing Systems		High Speed Computer Arithmetic
	Advanced Computer Architecture
	Memory Centric Computing for AI
	High Performance Computing
	Energy Harvesting for Electronic Systems.
Optimization and Security		Convex Optimization and Reinforcement Learning for Engineering Applications
	Blockchain Applications, Design, and Systems
	Network and Cyber Physical Systems Security
Communications and Networks		Advanced Digital Communications
	Advanced Concepts in Stochastic Processes, Detection, and Estimation Theory
	Broadband Communication Systems
	Optical Wireless Communication System
	Native AI for Networks

Category	Credit hours Required
Core Courses	12
Technical Electives	24
PhD Research Seminar I	0
PhD Research Seminar II	0
PhD Written Qualifying Exam	0
PhD Research Proposal Exam	0
PhD Research Dissertation	36
Total	72

Code	Title	Cr.
	Research Methods in Engineering	3
Select three courses from the list below:
	Digital Signal Processing	3
	Digital ASIC Design	3
	Machine Learning and Applications	3
	Advanced Computer Networks	3
	Communication Systems and Networks	3

Theme	Code	Course Title
Computing Systems		High Speed Computer Arithmetic
	Advanced Computer Architecture
	Memory Centric Computing for AI
	High Performance Computing
	Energy Harvesting for Electronic Systems.
Optimization and Security		Convex Optimization and Reinforcement Learning for Engineering Applications
	Blockchain Applications, Design, and Systems
	Network and Cyber Physical Systems Security
Communications and Networks		Advanced Digital Communications
	Advanced Concepts in Stochastic Processes, Detection, and Estimation Theory
	Broadband Communication Systems
	Optical Wireless Communication System
	Native AI for Networks

COURSE DESCRIPTION

CCEN 789 Communication Systems and Networks

CCEN 711 Energy Harvesting and Interface Circuits for Electronic Systems

CCEN 715 Memory Centric Computing for Artificial Intelligent

CCEN 733 High Speed Computer Arithmetic

CCEN 734 Advanced Computer Architecture

CCEN 741 Advanced Digital Communications

CCEN 742 Advanced Concepts in Stochastic Processes, Detection, and Estimation Theory

CCEN 743 Broadband Communication Systems

CCEN 744 Optical Wireless Communication Systems

CCEN 784 Digital Signal Processing

CCEN 785 Digital ASIC Design

CCEN 745 Blockchain Applications, Design, and Systems

CCEN 746 Convex Optimization and Reinforcement Learning for Engineering Applications

COSC 732 Machine Learning and Applications

COSC 737 Network and Cyber Physical Systems Security

COSC 738 High Performance Computing

COSC 788 Advanced Computer Networks

CCEN 747 Native AI for Network

STUDY PLAN

PEOPLE

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Title	Communication Systems and Networks
Code	CCEN 789
Loading	3 Credit-hours
Pre-Requisite	Undergraduate knowledge of communications systems, signals and systems (or equivalent)
Instructor	Arafat Al-Dweik
Catalog Description	This course covers the main concepts of digital data transmission. The topics covered will provide the student with thorough understanding of communications waveforms, transmitter and receiver design, wireless channels modeling, and communications networks.
Goal	To develop an understanding of concepts involved in the design of digital communications systems and networks.
Contents	The main topics are: Overview of wireless communications, pathloss and channel models, digital modulation and detection, performance of digital modulation system, channel coding, multicarrier modulation, multiuser systems, overview of cellular networks.
Recommended Textbooks	Andrea Goldsmith, Wireless Communications. Cambridge University Press; 2005, ISBN- 978-0521837163. (New Edition will come out)
Recommended References & Supplemental Material	1. Bernard Sklar and Fredric J. Harris, Digital Communications: Fundamentals and Applications, 3rd ed., Pearson, 2021, ISBN-13: 9780137569076. 2. John G. Proakis and Masoud Salehi, Digital Communications, 5th ed., McGraw Hill, 2008, ISBN: 978鈥�007-126378-8
Assessment	Course work 30%, midterm exam 30%, and final exam 40%.
Teaching and Learning Methodologies	The course delivery includes lectures, class discussions, and coursework. Demonstrations using Matlab and Simulink will be provided in class.
Course Learning Outcomes	1. Design a communications system modulator, demodulator, encoder, and decoder. 2. Apply probability theory to evaluate the performance wireless systems. 3. Compare various types of modulation and coding techniques. 4. Design basic multiuser communications networks.

Title	Energy Harvesting and Interface Circuits for Electronic Systems
Code	CCEN 711
Loading	3 Credit-hours
Pre-Requisite / Co-Requisite	CCEN 210 Digital Logic Design (or equivalent) and ELCN 322 Electronic Circuits and Devices (or equivalent)
Instructor	Baker Mohammad
Catalog Description	This course delves into the principles and operations of energy harvesting sources, bolstering energy resources and management for mobile devices. It covers Solar, triboelectric, piezoelectric, and thermoelectric energies. Topics include harvester impact, power transfer via conduction or RF, and interface circuits (DC to DC and AC to DC) for each source. The course explores energy conversion methods and mixer circuit design. Emphasis is on power management for efficiency. Additionally, it examines task management in operating systems (RTOS) for optimized energy performance. Students engage in research and simulations, focusing on energy harvesting, conversion, and management.
Goal	To develop an understanding of energy harvesting for electronics systems. Address contemporary issues in energy harvesting, transfer, storage and power management to enable autonomous operation. Analyses and designs interface circuits to transfer harvested energy into prospective loads. Analyze and identify interactions between software (operating system) and hardware, with an emphasis on task scheduling and priorities.
Contents	Energy harvesting sources for electronics, principle operation and impact of material and geometry Interface circuits of each energy harvester Efficiency calculation and optimization Power management with energy harvesting Operating system scheduling and awareness of energy availability
Recommended Textbooks	Energy Harvesting for Self-Powered Wearable Devices (Analog Circuits and Signal Processing) 1st ed. 2018 Edition by Mohammad Alhawari et. al. ISBN-13 978-3319625775
Recommended References & Supplemental Material	Advances in Energy Harvesting Methods Hardcover 鈥� February 16, 2013, by Niell Elvin (Editor), Alper Erturk (Editor) ISBN-13: 978-1461457046 ISBN-10: 1461457041 Edition: 2013^th Energy Harvesting for Autonomous Systems (Smart Materials, Structures, and Systems) Hardcover 鈥� June 30, 2010, by (Author, Editor), (Editor), ISBN-13: 978-1596937185 ISBN-10: 1596937181 Edition: 1st
Assessment	Two-semester exams: Two in-class presentations % Course project:
Teaching and Learning Methodologies	The course is delivered through PowerPoint-enhanced lectures and in-class student preparations. The students have access to all course materials via various digital and online platforms. Students can access the required simulation software including Matlab/Simulink, Verilog, FPGA, and ASIC tools, at various KU labs
Course Learning Outcomes	Analyze and compare energy harvesting sources and their application in mobile electronics devices. Identify technology challenges and new trends in portable electronics devices, including requirements for small size, energy density, and long lifetime. Research a contemporary issue related to energy harvesting and analyze the solution Assess the performance of electronic systems under different operation modes and limited energy sources Design of interface and combined circuits to energy harvesting Electronics devices Analyze the interaction between software and hardware of ES as it relates to task scheduling and energy management

Title	Memory Centric Computing for Artificial Intelligent
Code	CCEN听715
Loading	3听Credit-hours
Pre-Requisite	Graduate听level听course听in听Digital Design or VLSI
Instructor	Baker Mohammad
Catalog听Description	This course focus on computer memory design and its impact on performance, energy, and cost. 听Students will explore the design of with traditional memory technologies such as SRAMs, DRAMs, Multi-ported RAMs, CAMs, and Flash memory, alongside emerging ones such as Resistive RAM (memristor). The course presents memory classes, hierarchy, and architectural principles, while addressing cutting-edge concepts in in-memory and near-memory computing architectures for popular AI algorithms.
Goal	The objective is to provide insights into the significance of memory within digital systems, encompassing diverse memory types and the concept of memory hierarchy. Additionally, the course focuses on investigating the potential of new computing hardware utilizing In-Memory Computing.
Contents	Memory technologies and cell types (RAM, DRAM) Memory organization and periphery circuits Memory architecture and its.impact to system performance, cost, and power Emerging memories In-memory computing Sample implementation of a memory intensive algorithm 听
Recommended Textbooks	Baker Mohammad and Yasmin Halawani, In-Memory Computing Hardware Accelerators for Data-Intensive Applications, Springer cham, 2024, , ISBN 978-3-031-34232-5.
Recommended References听& Supplemental Material	In-Near-Memory Computing: Synthesis Lectures on Computer Architecture, 2021, Vol. 16, No. 2 , Pages 1-140, 听( 听and听, Artificial Intelligence Hardware Design: Challenges and Solutions, Wiley-Blackwell, 2021, ISBN-10鈥�:鈥幪�1119810450, ISBN-13: 978-1119810452 Selected journal and conference articles.
Assessment	Two semester exams:听 Two in-class presentations % Course project:
Teaching听and Learning Methodologies	The听course听is听delivered听through听PowerPoint-enhanced lectures听and in-class student听preparations.听The听students听have听access听to听all course听materials via various digital and online platforms. Students can access the required听simulation听software听including Matlab/Simulink, Verilog, FPGA, and ASIC tools, at various KU labs.
Course听Learning Outcomes	Recognize memory impact on digital system performance and specs. Analyze memory hierarchy and type on data storage, and computing. Evaluate design constraints and options for implementing a memory sub system. Understand contemporary trends in memory technology and design. Design and evaluate memory intensive operations.

Title	High Speed Computer Arithmetic
Code	CCEN 733
Loading	3 Credit-hours
Pre-Requisite / Co-Requisite	Undergraduate听knowledge听in Digital Design (or equivalent) and Computer Organization (or equivalent)
Instructor	Hani Saleh
Catalog Description	This course teaches the theory and design of high-performance logic circuits of arithmetic in computers. It covers various types of numbering systems such as fixed-point numbers, floating-point numbering system in addition to many computer arithmetic operations such as adders, multipliers, dividers.
Goal	To develop an understanding of computer arithmetic architectures, circuits and implementation challenges in terms of speed, area, power and energy consumption.
Contents	This course is designed for graduate students, aiming to provide a broad background on high speed computer arithmetic, it also offers substantial depth on arithmetic primitive鈥檚 design and optimization. The course will cover the background of number systems, adders, multipliers, dividers. Fixe-point and floating point arithmetic. A project will be used to enhance students鈥� practical capabilities on research, communication, and technical writing.
Recommended Textbooks	Behrooz Parhami, Computer Arithmetic: Algorithms and Hardware Designs, The Oxford Series in Electrical and Computer Engineering, 听2nd Edition, 2009, ISBN-13: 978-0195328486, ISBN-10: 0195328485 听听
Recommended References & Supplemental Material	Jean-Michel Muller 听.et .al, Handbook of Floating-Point Arithmetic 2010th Edition,听Birkh盲user, 听2010, ISBN-13: 978-0817647049 ISBN-10: 081764704X Israel Koren , Computer Arithmetic Algorithms, 2nd Edition, A K Peters/CRC Press, 2001, ISBN-13: 听978-1568811604 Mircea Vl膬du牛iu , Computer Arithmetic: Algorithms and Hardware Implementations, Springer, 2012, ISBN: 978-3642183140
Assessment	Project 30%听听(including a detailed literature review) Semester Exams听(3-tests) 30% Final听exam听40%.
Teaching and Learning Methodologies	The course delivery includes lectures, class discussions, and research papers and project work
Course Learning Outcomes	Acquire broad knowledge on computer arithmetic principles. Design fundamental arithmetic elements including adders, dividers and multipliers. Develop professional knowledge and research skills on several computer arithmetic primitives design. Synthesize computer arithmetic primitives in Verilog. Use cutting edge EDA tools for simulation, synthesis, place & route tools

Title	Advanced Computer Architecture
Code	CCEN 734
Loading	3 Credit-hours
Pre-Requisite	Graduate level course in Computer Architecture听
Instructor	Dr. Hani Saleh and 听听Dr. Ibrahim (Abe) M. Elfadel
Catalog Description	This course covers advanced topics in computer architecture with focus on emerging advancement in the field. A project will be used to enhance students鈥� practical capabilities on research, communication, and technical writing.
Goal	To provide students with the latest advances and developments of Computer Architecture.
Contents	Deeply pipelined processors, superscalar processors design, advanced instruction flow techniques, advanced register data flow techniques, complex pipelining, out of order execution, branch prediction, advanced superscalar architecture, cache coherency, multiprocessors, VLIW and parallel processors, multithreading, GPU and custom processors.
Recommended Text Books	Modern Processor Design: Fundamentals of Superscalar Processors, by听听and,听, McGraw-Hill, 2013, ISBN-13:听978-1478607830
Recommended References & Supplemental Material	Computer Architecture: A Quantitative Approach by David A. Patterson, The Morgan Kaufmann, 6^th听听edition, 2017 , ISBN-13: 978-0-12-811905-1 Computer Organization and Design: The Hardware/Software Interface, by David A. Patterson and John L. Hennessy, Morgan Kaufmann, 5^th听edition, 2013, ISBN-13:听978-0124077263听听 Many research papers will be available on the course Moodle page
Assessment	Two tests 25%, Midterm 35% and Project 40 %
Teaching and Learning Methodologies	The course delivery includes lectures, class discussions, and design project using
Course Learning Outcomes	Explain the principles behind superscalar microprocessor, speculative and out-of-order instruction execution. Use and apply several superscalar computer architecture design/analysis tools. Assess future trends in VLIW and parallel processor design. Analyze contemporary GPU architectures. Identify research directions, design and conduct experiments, analyze the data, and evaluate their research.

Title	Advanced Concepts in Stochastic Processes, Detection, and Estimation Theory
Code	CCEN 742
Loading	3 Credit-hours
Pre-Requisite	Graduate level course in Stochastic Processes, Detection, and Estimation
Instructor	Zhiguo Ding
Catalog Description	The aim of this course is to cover some advanced and important topics in stochastic processes, signal detection, and estimation. The course includes topics such as Detection of Random Signals with Unknown Parameters, Unknown Noise Parameters, Model Change Detection, Complex/Vector Extension, Bayesian Estimation, General Bayesian Estimators, Linear Bayesian Estimators, Estimation for Complex Data and Parameters.
Goal	Provide a comprehensive, deep, and overarching knowledge at the frontier of random signal analysis, detection & estimation theory.
Contents	The course includes topics such as Poisson & Associated Random Processes (RPs), Wiener Processes, Brownian Motion, Derivatives & Integrals of RPs, Karhunen-Loeve Expansion, Discrete-Time Markov Processes, Continuous-Time Markov Chains, Detection of Random Signals with Unknown Parameters, Detection of Signals with Unknown Noise Parameters, Least Squares Estimation, Method of Moments Estimation, Bayesian Estimation, and General Bayesian Estimators.
Recommended Textbooks	Class-notes distributed in class.
Recommended References & Supplemental Material	A.听Leon-Garcia, A., 鈥淧robability, Statistics and Random Processes for Electrical Engineers鈥�, Prentice-Hall, 3rd Edition, 2008. B.听S.M. Kay, 鈥淔undamentals of Statistical Signal Processing: Detection Theory鈥�, Prentice-Hall, Vol. 2, 1998. C.听S.M. Kay, 鈥淔undamentals of Statistical Signal Processing: Estimation Theory鈥�, Prentice-Hall, Vol. 1, 1993.
Assessment	Course work 30%, midterm exam 30%, and final exam 40%.
Teaching and Learning Methodologies	The course delivery includes lectures, class discussions, and coursework.
Course Learning Outcomes	Apply appropriate techniques to characterise and represent a stochastic process. Apply advanced mathematical methods to detect a random signal embedded in noise. Apply advanced mathematical methods to estimate the parameters of a random signal in a noisy environment.

Title	Digital Signal Processing
Code	CCEN 784
Loading	3 Credit-hours
Pre-Requisite	Undergraduate knowledge of Digital Signal Processing and Linear Algebra
Instructor	Paschalis Sofotasios
Catalog Description	This course is meant to be a second course in discrete-time signal processing. It provides a comprehensive treatment of signal processing methods to model discrete-time signals, design optimum digital filters, and to estimate the power spectrum of random processes. It includes topics such as signal models, parametric and nonparametric power spectrum estimation, optimal filters, the Levinson recursion, lattice filters, and Kalman filter.
Goal	To provide an in-depth knowledge of the analysis and design techniques for processing signals by digital methods.
Contents	Random variables, random vectors, DT random processes. Linear systems with random inputs, transformations using the eigen-decomposition, the discrete Karhunen-Loeve transform. Introduction to estimation theory. Signal Models: Least-squares method, all-pole modeling, pole-zero modeling, finite data records models, stochastic models. Parametric and Non-parametric Power Spectrum Estimation. The Levinson-Durbin recursion. FIR lattice filter, IIR lattice filters. Optimal filters: the FIR Wiener filter, the IIR Wiener filter, discrete Kalman filter, matched filters and eigen filters. Independent reading: Cepstral analysis and its application in signal processing
Recommended Textbooks	D.G. Manolakis, V.K. Ingle, S.M. Kogon, Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing, Artech House, 2005, ISBN-13: 978-1580536103, ISBN-10: 1580536107
Recommended References & Supplemental Material	M. Hayes, Statistical Digital Signal Processing and Modeling,听Wiley, 2008 (1996), ISBN-13: 听978-0471594314, ISBN-10: 听0471594318
Assessment	Course work 15%, project on independent reading (report and presentation) 25%, midterm exam 20%, and final exam 40%
Teaching and Learning Methodologies	The course delivery includes lectures, class discussions, and coursework.
Course Learning Outcomes	Understand concepts related to random signals and parameter estimation. Apply stochastic methods to find outputs of systems with random excitations. Develop models for random signals and analyze such signals in frequency domain. Design optimal FIR/IIR filters with predetermined characteristics. Implement cepstrum-based algorithms to perform tasks such as speech recognition. Apply statistical DSP algorithms for common engineering applications.

Title	Digital听ASIC听Design
Code	CCEN听785
Loading	3听Credit-hours
Pre-Requisite	Undergraduate听knowledge听of听Digital听Logic听Design
Instructor	Dr.听Hani听Saleh
Catalog听Description	This course teaches the students all the skills needed to perform Digital Application Specific Circuit (ASIC) design. It introduces the design flow and covers each step thoroughly to gain enough knowledge to design digital ASICs along with FPGAs. Digital circuit timing analysis is covered along with max and min timing calculations to design setup and hold violations free circuits. Furthermore the course uses state of the art EDA (Electronic Design Automation) tools such as Synopsys tools.
Goal	To听develop听an听understanding听of听structured听techniques听used听in听modeling, designing,听building听and听testing听complex digital听systems.
Contents	ASIC听design听flow:听role听of听HDL听in听ASIC听design.听HDL听coding听style for synthesis. ASIC听testing听and听test-bench听creation. Clocking听in ASIC design: various clocking听schemes and clock distribution听networks.听Single听clock听versus听multi-clock听designs. ASIC libraries: minimum set of cells needed for successful synthesis,听drive听strength,听wire听load听models,听cell听characterization. Constraints听for听synthesis:听clock听cycle,听clock听skew,听setup,听hold,听rise time, driving strength. Static听timing听analysis听(STA),听statistical听timing听analysis听and听chip variation. Floor-planning Place听and听Route听of听ASICs Parasitics,听noise听and听cross听talk Chip听filling听and听metal听filing Timing听closure听and听tapeout Failure听modes听and听models Test听and听design听for听testability Packing听issues Detailed literature survey for a real-life computer architecture problem to be implemented in their course project
Recommended Textbooks	Michael听D.听Ciletti,听Advanced听Digital听Design听with听Verilog听HDL, Prentice Hall, 2^nd听edition, 2010, ISBN: 978-0136019282 Khosrow Golshan , Physical Design Essentials: An ASIC Design Implementation听Perspective,听2007,听ISBN-10:听0387366423,听ISBN-13: 978-0387366425

Recommended References & Supplemental听Material	Joseph听Cavanagh,听Verilog听HDL:听Digital听Design听and听Modeling,听CRC, 2007, ISBN: 978-1420051544 Sanjay听Churiwala听and听Sapan听Garg,听Principles听of听VLSI听RTL听Design: A Practical Guide, Springer, 2011, ISBN: 978-1441992956 Pong听P.听Chu,听FPGA听Prototyping听By听Verilog听Examples,听Wiley,听2008, ISBN: 978-0470185322 John听Williams,听Digital听VLSI听Design听with听Verilog:听A听Textbook听from Silicon Valley Technical Institute, Springer, 2008, ISBN: 978- 1402084454 Michael听J.听Flynn听and听Wayne听Luk,听Computer听System听Design:听System- on-Chip, Wiley, 2011, ISBN: 978-0470643365 , Application-Specific Integrated Circuits,听Addison-Wesley听Publishing听Company,听1997,听ISBN:听978- 0321602756
Assessment	Semester Exams/Work 听20% Project 30%听听(including a detailed literature review) Midterm听exam听20% Final听exam听30%.
Teaching听and听Learning Methodologies	The course delivery includes lectures, class discussions, and design project听using听ASIC听design听EDA听tools听such听as听Cadence听and听Synopsys.
Course听Learning Outcomes	Apply听mathematical听methods听and听circuit听timing analysis听in digital circuits. Create听RTL听models听to听realize听high听speed听specified听digital听functions. Create听test听benches听and听integrate听design听for听testability听structures. Design听and听evaluate听a听significant听ASIC听project听having听a听set听of objective criteria and design constraints. Use听EDA听tools听to听perform听simulation,听synthesis,听placement,听routing and timing analysis.

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PhD in Computer and Information Engineering

Overview