Restricted Research - Award List, Note/Discussion Page

Fiscal Year: 2021

249  University of North Texas  (84545)

Principal Investigator: Zhao,Hui

Total Amount of Contract, Award, or Gift (Annual before 2011): $ 398,684

Exceeds $250,000 (Is it flagged?): Yes

Start and End Dates: - 2/29/24

Restricted Research: YES

Academic Discipline: Computer Science & Engineering

Department, Center, School, or Institute: College of Engineering

Title of Contract, Award, or Gift: REU Site: Interdisciplinary Research Experience on Accelerated Deep Learning through A Hardware-Software Collaborative Approach

Name of Granting or Contracting Agency/Entity: National Science Foundation
CFDA Link: NSF
47.070

Program Title: N/A
CFDA Linked: Computer and Information Science and Engineering

Note:

1.3.2 (SAM); IRB Protocol #IRB-21-26; This project proposes an interdisciplinary Research Experience for Undergraduates in Hardware-Accelerated Deep Learning at the University of North Texas with an emphasis in hardware-software codesign principles. The current proposal is to have 10 external undergraduate participants per year for three years. Each year, students will participate in a 10-week summer program. The proposed model includes round-the-clock mentoring by a team led by faculty mentors and graduate students, a streamlined onboarding process that lets participants start their research projects sooner, daily meetings with mentors to plan activities throughout the day, and training in software tools for quick turnaround of research goals. Participants learn the background knowledge, match themselves to a desired project topic, and spend significant time in focused research. They will be encouraged to follow through by working with their faculty mentors to write a paper or technical report on their project and present their work at a professional workshop. High quality papers will be submitted to research conferences and workshops. The proposed REU program aims to address the nation’s need for a diverse group of innovative researchers and professionals, who are capable of using a variety of techniques to address software and hardware issues in an emerging diverse domains of AI applications serving commercial, educational, and national security objectives.

Discussion: No discussion notes

 

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