Edge computing and machine learning on embedded systems
Machine learning algorithms have increased in popularity due to their use in solving complex, non-linear problems. However, the process of training and using algorithms typically requires expensive, general-purpose hardware. More attention is being turned to the use of such algorithms in embedded systems. This paper proposes a solution to enable the use of machine learning algorithms on embedded systems hardware.
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2021