Using commercial FPGAs as external accelerators for artificial neural networks in embedded applications
Date
2020-05
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Abstract
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.
Description
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
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Capstone project
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Keywords
machine learning, Field Programmable Gate Array (FPGA), embedded systems, Artificial Neural Networks (ANNs)