Neural Network-Based Self-Adjusting Computational Processors

Neural Network-Based Self-Adjusting Computational Processors

2020

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

Click here to view the project team’s poster

Watch the team’s final presentation (on Zoom) here:

 

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Mathematics

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Computer Science, Math, Statistics

Computer Science, Math

Statistics