Abstract

Development of State of the Art AI Vision Algorithm on Xilinx Alveo U-200 FPGA Cloud and CPU+GPU Platform

Highlights

  • Applications of computer vision involve replicating the vision seen by the computers to be visible to humans

  • Due to hardware limitation at that time, these ANN did not find its true potential. It was in the last decade, the driving force on ANN architecture modelling has paved its way for the improvements of CNN algorithms for computer vision application

  • CNN has been mainly used for computer vision applications

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Summary

INTRODUCTION

Applications of computer vision involve replicating the vision seen by the computers to be visible to humans. The term artificial neural network was first coined in the late 1950s [1] It is a computational model of the animal neuron in brains. Due to hardware limitation at that time, these ANN did not find its true potential It was in the last decade, the driving force on ANN architecture modelling has paved its way for the improvements of CNN algorithms for computer vision application. Various CNN architectures have been developed since to meet specific results in the field in terms of applications, hardware compatibilities and computational complexity. Hebb in 1940s [17] Though these ANN does not work as accurately and sensitive as biological neurons, it can replicate the same learning behaviour. This happens only when the aggregated electrochemical signal is higher than the synaptic threshold, it results in an electrochemical spike and moves down the axon to dendrites of other neurons

Components of ANN
Inference vs Training
CONVOLUTION NEURAL NETWORK
Building Blocks of CNN
AlexNet
Network in Network
VGG - 16
GoogLeNet
State of the art Object Detection Algorithms
HARDWARE AS AI ACCELERATORS
IMPLEMENTATIONS AND RESULTS
1) Alexnet Results
4) GoogLeNet Results
VI.REFERENCES
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