Hardware Benchmarking of Deep Neural Networks

Hardware Benchmarking of Deep Neural Networks

Internship Description

Artificial Intelligence is gaining popularity recently. Even though most of the efforts have been put from the software aspects, an efficient hardware execution environment is a must to realize them in practice. Two promising generic hardware solutions for efficient DNN realizations are FPGAs and GPUs. When both of them compared, FPGAs seem to provide the best efficiency/energy consumption. On the other hand, they cannot provide the enough accuracy and performance as GPUs can. Therefore, the correct hardware selection depends on the DNN specifications. At that point, benchmark results of these two architectures with respect to DNN types (e.g., detection, classification, localication, etc.) becomes very important.
 
Recommended Student Academic & Research Background: Basic knowledge of deep learning, programming, GPUs, FPGAs, AI, Programming​

Deliverables/Expectations

Objectives: To provide extensive benchmarking results and comparison between FPGAs and GPUs for different DNN types. Tools to learn: Pytorch, GPUs, NVIDIA Tools, FPGAs

Faculty Name

Khaled Nabil Salama

Field of Study

Electrical Engineering, Computer Science ​