Project Description
This project builds upon recently published work by Archlab that introduces an energy efficient framework for near-sensor convolutions with brain inspired temporal arithmetic. Students will learn how this framework can be extended to implement a wide class of neural networks. Using this knowledge the team will implement a state of the art image segmentation neural network (YOLO) and investigate the impact of temporal arithmetic on model performance and implementation. By the end of the project students will be able to visualize the impact of an emerging and non-traditional form of computation on AI.
Team Members
- Cooper Hawley
- Edward Gonzales
- Erik Rodriguez
- Jayden Jardine
Professor and Mentors
- Prof. Tim Sherwood
- Grad mentor: Rhys Gretsch