Development and Design Space Exploration of Deep Convolution Neural Network for Image Recognition

Description

– Development and Design Space Exploration of Deep Convolution Neural Network for Image Recognition –

Download Development and Design Space Exploration of Deep Convolution Neural Network for Image Recognition. Machine Learning in Planetary Science students who are writing their projects can get this material to aid their research work.

Abstract

Deep Neural Networks are now deployed for many modern artificial Intelligence applications including computer vision, speech recognition, self-driving cars, cancer detection, gaming and robotics.

Inspired by the mammalian visual cortex, Convolutional Neural Networks (CNNs) have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions.

Software simulations of CNNs are an efficient way to evaluate and explore the performance of the system. In this thesis, I present a software implementation and study of a Deep CNN for image recognition.

The parameterization of our design offers the flexibility to adjust the design in order to balance performance and flexibility, particularly for resource-constrained systems.

I also present a design space exploration for obtaining the implementation with the highest performance on a given platform.

Introduction

Neuro-Inspired systems are computing systems which are modelled after biological neuroprocesses in the brain of animals, especially higher animals like humans.

Such processes have cognitive abilities which make the animal intelligent, in the sense that they are able to make decisions having processed data they receive from their environment or with changes involved in their body system.

Biological beings are not born with the ability to make decisions after perceiving stimuli but they attentively perform an activity when they continually process such novel stimulus;

and such neurological cognition is the ability that neuro-inspired systems want to leverage on and mimic, if not surpass. Artificial intelligence (AI) is a branch of computer science that emphasizes the creation of intelligent agents that work and perceive as humans do (Techopedia, 2017).

Machine learning (ML) algorithms are learning algorithms for AI and have to do with the mathematical models used to build software that progressively modifies its algorithms so as to improve future results, that is software with the ability to learn.

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