FPGA-Based Acceleration of the Self-organizing Map (SOM) Algorithm Using High-level Synthesis

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" FPGA-Based Acceleration of the Self-organizing Map (SOM) Algorithm Using High-level Synthesis "
Oninda, Mohammad Abdul Moin Khalid, Mohammed AS

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1105794
Doc. No	:	TLpq2323921782
Main Entry	:	Khalid, Mohammed AS
	:	Oninda, Mohammad Abdul Moin
Title & Author	:	FPGA-Based Acceleration of the Self-organizing Map (SOM) Algorithm Using High-level Synthesis\ Oninda, Mohammad Abdul MoinKhalid, Mohammed AS
College	:	University of Windsor (Canada)
Date	:	2019
student score	:	2019
Degree	:	M.A.Sc.
Page No	:	110
Abstract	:	One of the fastest growing and the most demanding areas of computer science is Machine Learning (ML). Self-Organizing Map (SOM), categorized as unsupervised ML, is a popular data-mining algorithm widely used in Artificial Neural Network (ANN) for mapping high dimensional data into low dimensional feature maps. SOM, being computationally intensive, requires high computational time and power when dealing with large datasets. Acceleration of many computationally intensive algorithms can be achieved using Field-Programmable Gate Arrays (FPGAs) but it requires extensive hardware knowledge and longer development time when employing traditional Hardware Description Language (HDL) based design methodology. Open Computing Language (OpenCL) is a standard framework for writing parallel computing programs that execute on heterogeneous computing systems. Intel FPGA Software Development Kit for OpenCL (IFSO) is a High-Level Synthesis (HLS) tool that provides a more efficient alternative to HDL-based design. This research presents an optimized OpenCL implementation of SOM algorithm on Stratix V and Arria 10 FPGAs using IFSO. Compared to recent SOM implementations on Central Processing Unit (CPU) and Graphics Processing Unit (GPU), our OpenCL implementation on FPGAs provides superior speed performance and power consumption results. Stratix V achieves speedup of 1.41x - 16.55x compared to AMD and Intel CPU and 2.18x compared to Nvidia GPU whereas Arria 10 achieves speedup of 1.63x - 19.15x compared to AMD and Intel CPU and 2.52x compared to Nvidia GPU. In terms of power consumption, Stratix V is 35.53x and 42.53x whereas Arria 10 is 15.82x and 15.93x more power efficient compared to CPU and GPU respectively.
Subject	:	Computer engineering
	:	Electrical engineering