Using commercial FPGAs as external accelerators for artificial neural networks in embedded applications

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Artificial Neural Networks (ANNs) is a branch of Machine Learning that has seen recent widespread adoption for solving computational problems that seem impractical to solve with traditional algorithmic approaches. ANNs have achieved high accuracy on tasks such as facial recognition, object detection and speech recognition. And recently, ANNs have also seen applications in embedded systems, where it has been used to train robots to learn from their environment and cameras to detect faces in a crowd. However, achieving reasonable performance on a traditional microcontroller can be difficult since ANNs are computationally expensive. This paper investigates the possibility of using a Field Programmable Gate Array (FPGA) as an external accelerator for a microcontroller unit. The aim is for the combined performance of the FPGA and the microcontroller, for running the ANN, to be better than just the microcontroller. For the tested neural network, the results show that the combined system with the FPGA and microcontroller runs at more than twice the speed of a system with just a microcontroller.
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 2020
machine learning, Field Programmable Gate Array (FPGA), embedded systems, Artificial Neural Networks (ANNs)