Deep Learning on FPGAs: evaluating the suitability of using FPGAs to accelerate deep learning algorithms at Manchester Metropolitan University on FindAPhD.com

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Dr H Ji Applications accepted all year round Self-Funded PhD Students Only

About the Project

Deep learning algorithms use extremely large data sets thus are computational extensive. GPGPUs have been used to accelerate. With the wider adoption and refinement of heterogeneous computing language such as openCL, acceleration on FPGAs may provide a closer model to higher level programming languages, and better power performance in which FPGAs are excel.

The aim of the project is to evaluate the suitability of using FPGAs to accelerate deep learning algorithms. Especially it will achieve the following objectives:
• Review deep learning algorithms and identify suitable candidate for acceleration on FPGA.
• Use openCL to implement the algorithm on Altera FPGAs.
• Evaluation the timing and power performances against those published in literature, focusing on one type of applications such as computer vison.

Funding Notes

A 2:2 (Hons) or above degree in Computer Engineering, Electrical and Electronic Engineering or relevant subjects are required. Knowledge with designing using GPU and/or FPGA, and machine learning is preferred.

PLEASE NOTE: CVS ARE NOT ACCEPTED, PLEASE COMPLETE THE APPLICATION IN THE LINK BELOW. CVS MAY BE ATTACHED AS A SUPPORTING DOCUMENT

Deep Learning on FPGAs: evaluating the suitability of using FPGAs to accelerate deep learning algorithms

Manchester Metropolitan University Advanced Materials and Surface Engineering

This project is no longer listed on FindAPhD.com and may not be available.

About the Project

Funding Notes

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