Rapid increase in using artificial intelligence (AI) in different applications requires considering high-level of accuracy and performance in addition to energy efficiency especially for edge devices as the main design constraints.
Machine learning models for AI are trained based on application-specific data and then deployed in target execution platform to perform useful predictions. Machine learning algorithms are computationally intensive so, deployment of these algorithms for embedded systems is very challenging as not only achieving the required system performance and prediction accuracy are essential, reducing energy consumption is also critical. The aim of this research is to investigate the computational complexity of machine learning algorithms, especially, deep learning algorithms for embedded applications (such as object recognition) and develop a reconfigurable hardware accelerator, which is implemented on FPGA to satisfy the required performance and accuracy while minimizing the energy consumption.
Undergraduate
Students should have passed COMPSYS 304 and COMPSYS 305 very well, as good knowledge of computer architecture and digital systems design is essential for this project.
Students are highly recommended to take COMPSYS 701 as an elective, which can be helpful to provide additional knowledge to carry out this research.
Embedded Systems (405.760, Lab)