Computer Engineering MS

Computer Engineering (thesis option), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
CMPE-610
Analytical Topics in Computer Engineering
This course begins by reviewing signal and system analysis techniques for analyzing linear systems. It includes Fourier techniques and moves on to present fundamental computational techniques appropriate for a number of applications areas of computer engineering. Other topics include symbolic logic and optimization techniques. (Prerequisites: CMPE-480 and (MATH-251 or 1016-345) or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
3
CMPE-795
Graduate Seminar
The graduate seminar prepares graduate students to effectively conduct their thesis research and expose them to current research in various areas of computer engineering. Current literature topics are reviewed through interactive presentations and discussions. (This class is restricted to students in the CMPE-MS, CMPE-BS/MS program.) Seminar (Fall, Spring).
0
Choose one of the following flexible core courses:
3
   CMPE-630
   Digital Integrated Circuit Design
This course will cover the basic theory and techniques of Digital Integrated Circuit Design in CMOS technology. Topics include CMOS transistor theory and operation, design and implementation of CMOS circuits, fabrication process, layout and physical design, delay and power models, static and dynamic logic families, testing and verification, memory and nanoscale technologies. Laboratory assignments and project facilitate in hands-on learning of circuit-level design and simulation, layout and parasitic extractions, pre and post-layout verification and validation, full-custom flow and Synthesis based flow, using industry standard CAD tools. (Prerequisites: CMPE-260 and EEEE-282 and (EEEE-380 or EEEE-381) or equivalent courses.) Lab 2, Lecture 3 (Fall, Spring).
 
   CMPE-660
   Reconfigurable Computing
The objective of this course is to present the foundations of reconfigurable computing methodologies from both hardware and software perspectives. Topics covered are: architectures of modern field programmable gate arrays (FPGAs), digital system design methodologies using FPGAs, hardware-software co-design with embedded processors, hardware optimization techniques, system level integration under operating system, dynamic reconfiguration. Laboratory projects in which students will acquire a solid capability of Xilinx CAD tools and FPGA devices are required. The projects include the whole design flow: design of the system, VHDL modeling, software and hardware development, FPGA verification. (Prerequisites: CMPE-260 or equivalent course or graduate standing in the CMPE-MS program.) Studio 3 (Fall).
 
   CMPE-755
   High Performance Architectures
This course will focus on learning and understanding the available hardware options to satisfy the needs of high performance and computational intensive applications. Special attention will be paid to single platform massively parallel devices, their programming and efficient use of the hardware resources. The course will include hands on work with the actual device, lab work, and technical reports and conference paper reading as a relevant source information. (Prerequisite: CMPE-350 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Fall).
 
Choose one of the following flexible core courses:
3
   CMPE-655
   Multiple Processor Systems
The course introduces basic concepts of parallel and high-performance computing and current methodologies and trends in the design and programming of multiprocessor systems. Theoretical models of parallel computing and performance metrics are studied and contrasted with practical parallel system architectures, programming environments, and benchmarking techniques. Parallel architectures are classified according to mode and degree of parallelism, memory organization, and type and typology of interconnection networks used in the design. The suitability of various architectures in meeting demands is studied in depth including the study of representative examples of current commercial machines. Students will complete programming assignments on a parallel computer illustrating practical issues. A review and analysis of a commercial parallel processor system or an active research area is required; written review presented in class. (Prerequisite: CMPE-550 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
 
   CMPE-670
   Data and Communication Networks
This course will give an overview of the technologies, architectures and protocols used to build various types of computer and communication networks - wired or wireless. The emphasis will be placed on discussions of various network design problems and solution approaches. Specific issues covered in this course include: framing and coding, error detection, multiple access control, addressing, routing, flow and congestion control, scheduling and switching. (Prerequisite: MATH-251 or 1016-345 or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
 
   CMPE-677
   Machine Intelligence
Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning). (Prerequisites: CMPE-380 and CMPE-480 and MATH-251 or graduate standing in the CMPE-MS, CMPE-BS/MS program.) Lecture 3 (Fall).
 
 
Graduate Electives*
9
Second Year
CMPE-790
Thesis
Thesis research investigates an independent problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty adviser to guide the thesis before registering. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
9
 
Graduate Elective
3
Total Semester Credit Hours
30

* At least two graduate electives must come from the computer engineering department.

Computer Engineering (project option), MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
CMPE-610
Analytical Topics in Computer Engineering
This course begins by reviewing signal and system analysis techniques for analyzing linear systems. It includes Fourier techniques and moves on to present fundamental computational techniques appropriate for a number of applications areas of computer engineering. Other topics include symbolic logic and optimization techniques. (Prerequisites: CMPE-480 and (MATH-251 or 1016-345) or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
3
CMPE-795
Graduate Seminar
The graduate seminar prepares graduate students to effectively conduct their thesis research and expose them to current research in various areas of computer engineering. Current literature topics are reviewed through interactive presentations and discussions. (This class is restricted to students in the CMPE-MS, CMPE-BS/MS program.) Seminar (Fall, Spring).
0
Choose one of the following flexible core courses:
3
   CMPE-630
   Digital Integrated Circuit Design
This course will cover the basic theory and techniques of Digital Integrated Circuit Design in CMOS technology. Topics include CMOS transistor theory and operation, design and implementation of CMOS circuits, fabrication process, layout and physical design, delay and power models, static and dynamic logic families, testing and verification, memory and nanoscale technologies. Laboratory assignments and project facilitate in hands-on learning of circuit-level design and simulation, layout and parasitic extractions, pre and post-layout verification and validation, full-custom flow and Synthesis based flow, using industry standard CAD tools. (Prerequisites: CMPE-260 and EEEE-282 and (EEEE-380 or EEEE-381) or equivalent courses.) Lab 2, Lecture 3 (Fall, Spring).
 
   CMPE-660
   Reconfigurable Computing
The objective of this course is to present the foundations of reconfigurable computing methodologies from both hardware and software perspectives. Topics covered are: architectures of modern field programmable gate arrays (FPGAs), digital system design methodologies using FPGAs, hardware-software co-design with embedded processors, hardware optimization techniques, system level integration under operating system, dynamic reconfiguration. Laboratory projects in which students will acquire a solid capability of Xilinx CAD tools and FPGA devices are required. The projects include the whole design flow: design of the system, VHDL modeling, software and hardware development, FPGA verification. (Prerequisites: CMPE-260 or equivalent course or graduate standing in the CMPE-MS program.) Studio 3 (Fall).
 
   CMPE-755
   High Performance Architectures
This course will focus on learning and understanding the available hardware options to satisfy the needs of high performance and computational intensive applications. Special attention will be paid to single platform massively parallel devices, their programming and efficient use of the hardware resources. The course will include hands on work with the actual device, lab work, and technical reports and conference paper reading as a relevant source information. (Prerequisite: CMPE-350 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Fall).
 
Choose one of the following flexible core courses:
3
   CMPE-655
   Multiple Processor Systems
The course introduces basic concepts of parallel and high-performance computing and current methodologies and trends in the design and programming of multiprocessor systems. Theoretical models of parallel computing and performance metrics are studied and contrasted with practical parallel system architectures, programming environments, and benchmarking techniques. Parallel architectures are classified according to mode and degree of parallelism, memory organization, and type and typology of interconnection networks used in the design. The suitability of various architectures in meeting demands is studied in depth including the study of representative examples of current commercial machines. Students will complete programming assignments on a parallel computer illustrating practical issues. A review and analysis of a commercial parallel processor system or an active research area is required; written review presented in class. (Prerequisite: CMPE-550 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
 
   CMPE-670
   Data and Communication Networks
This course will give an overview of the technologies, architectures and protocols used to build various types of computer and communication networks - wired or wireless. The emphasis will be placed on discussions of various network design problems and solution approaches. Specific issues covered in this course include: framing and coding, error detection, multiple access control, addressing, routing, flow and congestion control, scheduling and switching. (Prerequisite: MATH-251 or 1016-345 or graduate standing in the CMPE-MS program.) Lecture 3 (Fall, Spring).
 
   CMPE-677
   Machine Intelligence
Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning). (Prerequisites: CMPE-380 and CMPE-480 and MATH-251 or graduate standing in the CMPE-MS, CMPE-BS/MS program.) Lecture 3 (Fall).
 
 
Graduate Electives*
9
Second Year
CMPE-792
Graduate Project
Graduate Project is a scholarly undertaking that addresses an immediate and practical problem with tangible outcomes. A formal report, presentation, or demonstration is required. The student must obtain the approval of an appropriate faculty adviser to guide the project before registering. (This class is restricted to students in the CMPE-MS, CMPE-BS/MS program.) Project (Fall, Spring, Summer).
3
 
Project Focus Electives
6
 
Graduate Elective*
3
Total Semester Credit Hours
30

* At least two graduate electives must come from the computer engineering department.