This 7-week RET program on the University of Notre Dame campus is projected to run 40-hours per week June 18 to August 3, 2018. Benefits include:
- A stipend of $6300
- A stipend of $500 throughout the Academic Year to participate in ongoing professional development
- A stipend of $1500 during the Academic Year for materials to implement developed K-12 curricular module
- 3 non-degree graduate credit hours
(Please contact Michael Niemier with any additional questions; firstname.lastname@example.org; 574-631-3858)
An overview of possible projects can be found here.
Examples of recent RET research projects and associated classroom modules can be found here.
(Please contact email@example.com for additional information and/or resources.)
Historically, most information processing is done in the context of the von Neumann model. With the von Neumann model, we process information by writing software (or code) to describe an algorithm that can solve a problem of interest. Code is then compiled – i.e., broken down into a sequence of instructions that a microprocessor understands. Step functionality (i.e., digital instruction encodings) and the digital data for the instructions are stored in a microprocessor’s memory, and are in turn fetched and executed. After some time, the problem’s solution is generated. However, it is widely perceived that the von Neumann model will not be able to sustain the historical performance scaling trends that industry and society have come to expect. As such – and driven in part by the presidential executive order that put forth the National Strategic Computing Initiative (NCSI) [1, 2] – researchers are working to develop new ways to process information, that either offer new scaling paths and/or can perform similar computational tasks with much less energy.
The focus of this RET is to help prepare high school students for the evolution of information processing systems. We will focus on computational hardware and software models that (i) are inspired by biological systems (with an emphasis on convolutional neural networks associated with deep learning algorithms that are already driving commercialized research at Google, Facebook, etc.), (ii) analog architectures inspired by the retina that can be more easily realized with new information processing technologies, and/or (iii) that directly exploit the evolution of a physical system to solve a problem of interest.
Research projects should be suitable for teachers from many disciplines including biology, math, physics, computer science/digital electronics, etc.
 The White House. (October 10, 2015). A Nanotechnology-Inspired Grand Challenge for Future Computing.
 The White House. (July 29, 2015). Executive Order -- Creating a National Strategic Computing Initiative.