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