Ronald F. DeMara

Courses Taught	Curriculum Enhancement	Curriculum Grants	Laboratory Development	Teaching-Related Publications

Courses Taught

Topic areas taught at the Undergraduate and Graduate levels:

• Computer Architecture
• Machine Learning
• Parallel Processing
• Networks/Modeling/Simulation

Courses offered in lecture, recitation, and seminar-style formats at Undergraduate (EEL3xxx/4xxx) and Graduate (EEL5xxx/6xxx) levels with typical [class size] listed below:

• Undergraduate Courses:
• EEL3801: Computer Organization with Laboratory [60]
  • – Computer arithmetic, Instruction Set Architecture, performance, data path
• EEL4767: Computer System Design I with Laboratory [40]
  • – Computer Organization, Microprocessor Systems
• EEL4768: Computer System Design II with Laboratory [30]
  • – Computer Architecture, Data Path Design
• EEL4851: Engineering Data Structures with Laboratory [50]
  • – Data Structures and Algorithms in C++ and now Java
• EEL4882: Engineering Systems Software [30]
  • – Operating Systems Concepts, Process Scheduling, Resource Management
  • – offered in live-only and live-with-web-based formats
• EEL4818: Machine Learning I [15]
  • – Decision Trees, Evolvable Hardware, Neural Networks
  • – Team taught with 3 faculty: taught module on Evolvable Hardware
• EEL4817: Machine Learning II [10]
  • – Honors course for undergraduates developing Machine Learning projects
  • – Team taught with 3 faculty: supervised autonomous FPGA projects

Graduate Courses:
• EEL5708: High Performance Computer Architecture [40]
  • – Pipelining/Branch Prediction, Superscalar Architecture, Cache Design
  • – offered in live-only and live-with-remote-video plus web-based formats
• EEL6707: Parallel Processing [20]
  • – Distributed/Shared Memory, Interconnection Networks, Data Transformations
  • – offered in live-only and live-with-remote-video plus web-based formats
• EEL6763a: Current Topics - Scalable Shared-Memory Architectures [20]
  • – Data Consistency, Cache Coherence, Profiling and Metrics
  • – offered in live-with-remote-video format
• EEL6763b: Current Topics - Clockless Processor Design [20]
  • – Asynchronous ALU Design, Fine-grained and Coarse-grained Dataflow
• EEL6763c: Current Topics - Autonomously Reconfigurable and Evolvable Hardware [20]
  • – FPGA-based Intrinsic/Extrinsic Evolution, Autonomous Regeneration
  • – offered in live-with-remote-video plus web-based format
• EEL6769: Parallel Knowledge Processing [15]
  • – Marker-propagation Architectures, Classifier Systems, Genetic Algorithms

Classes are available in live-only, live-with-remote-video, and/or web-based formats. Honors courses at the undergraduate-level and research courses at the graduate-level typically include a student research project and report to creatively apply the concepts.

Back to Top

---

Curriculum Enhancement

Courses developed that are now available in the University Catalog:

• -EEL5708: High Performance Computer Architecture  (offered Fall semester)
• -EEL6707: Parallel Processing  (offered Spring semesters)
• -EEL6769: Parallel Knowledge Processing  (offered Spring semesters)
• -EEL5762: Computer Systems Performance Analysis  (availablity varies)
• -EEL4818H: Machine Learning I  (team-developed - offered Fall semesters)
• -EEL4817H: Machine Learning II  (team-developed - offered Spring semesters)

Special topics course offerings (offered Fall semesters):

• -EEL6763a: Scalable Shared-Memory Architectures  (previous offering)
• -EEL6763b: Clockless Processor Design  (current offering)
• -EEL6763c: Autonomously Reconfigurable and Evolvable Hardware  (current offering)

Virtual laboratories using labview now available for EEL3342 lab and EEL4767 lab.

Back to Top

---

Curriculum Grants

The current NSF grant is for Combined Research Curriculum Development (CRCD) on Machine Learning Advances for Engineering Education. It extends research on Evolvable Hardware and also develops curriculum materials from the results for nationwide dissemination. As the first educational component of this project, Machine Learning Modules have been developed that were taught them to 243 students in 8 undergraduate classes. This includes course modules for sophomore and junior courses such as Introduction to Computer Engineering and Data Structures which replace arbitrary programming assignments with a progression of case studies in Machine Learning.

The second education component is developing the senior-level course sequence entitled Machine Learning I and Machine Learning II that have been taught to 34 students. Students are motivated to enroll in these courses as electives or honors courses based on their exposure to the Machine Learning Modules in their core courses.

The third educational component is related to nationwide dissemination including a website of materials produced, homeworks/projects assigned and code produced for approximately 20 undergraduate research projects, three Masters with thesis, 1 Ph.D., 14 conference papers (8 technically-oriented venues and 6 educationally-oriented venues), and 3 journal papers. A Springer Verlag Lecture Notes in Computer Science book has been approved.

A self-contained Evolvable Hardware module was created for the Data Structures course. In this module, Offline Evolution with a simplified FPGA simulator is integrated into the curriculum. Rather than learning adjacency matrix data structures using arbitrary examples, students have these concepts illustrated using graph representations of digital circuits. The laboratory modules developed apply the concepts to a FPGA architecture and utilize an evolvable hardware strategy to optimize an adder circuit and track how the corresponding data structure changes during the evolutionary process. The full-semester advanced graduate course entitled Evolvable and Autonomously Reconfigurable Hardware has been taught twice.

Back to Top

---

Laboratory Development

Laboratory infrastructure grants have been obtained to enhance facilities including Sun servers, workstations, and an 8-way shared-memory multiprocessor. He is the Founder and Director of the Computer Architecture Lab, and co-developed or renovated the Microprocessor Lab, Open Computing Lab, Intelligent Systems Lab, and VLSI Lab. A additional lab enhancement project was directing the integration of National Instrument’s Labview PC-based virtual instrumentation breadboard environment into 2 undergraduate laboratories: EEL3342: Digital Systems Design and EEL4767: Computer Systems Design I.

Back to Top

---