Project Description

The automated neural network emulator is my senior project at the University of Utah currently being developed by myself and the Laboratory of Circuits and Systems (LCAS) at the University of Utah.  The project seeks to develop an automated machine learning framework that can quickly deploy and test different neural networks onto an FPGA to simulate their performance if they were implemented onto an analog neural network accelerator.  This project will involve software to take existing state of the art neural networks and modify their behavior to simulate the effects of implementing the neural networks onto an analog neural network accelerator.  This software should be capable of deploy and testing a neural network onto an FPGA in an automated process that is both dataset and network agnostic.

As this project is still in development, there are minimal photos of it.  The project is expected to be finished by May of 2025 as that is when I am to graduate from the University of Utah.  If this project isn't finished, then I'm not graduating, thus it should be finished by then!

As more information about this project becomes available, this webpage will be updated accordingly.  Thank you for your patience!

Code Repository

As work on this project is currently being done, there is no public GitHub repository yet.  However, when this project is finished, a GitHub link will be made available.