Artificial Intelligence Applications and Reconfigurable Architectures

Anuradha D. Thakare, Sheetal Umesh Bhandari

• Publisher: Wiley-Scrivener
• Year: 2023
• Print ISBN: 9781119857297
• eBook ISBN: 9781119857884
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

ARTIFICIAL INTELLIGENCE APPLICATIONS and RECONFIGURABLE ARCHITECTURES

The primary goal of this book is to present the design, implementation, and performance issues of AI applications and the suitability of the FPGA platform.

This book covers the features of modern Field Programmable Gate Arrays (FPGA) devices, design techniques, and successful implementations pertaining to AI applications. It describes various hardware options available for AI applications, key advantages of FPGAs, and contemporary FPGA ICs with software support. The focus is on exploiting parallelism offered by FPGA to meet heavy computation requirements of AI as complete hardware implementation or customized hardware accelerators. This is a comprehensive textbook on the subject covering a broad array of topics like technological platforms for the implementation of AI, capabilities of FPGA, suppliers' software tools and hardware boards, and discussion of implementations done by researchers to encourage the AI community to use and experiment with FPGA.

Readers will benefit from reading this book because

• It serves all levels of students and researcher's as it deals with the basics and minute details of Ecosystem Development Requirements for Intelligent applications with reconfigurable architectures whereas current competitors' books are more suitable for understanding only reconfigurable architectures.
• It focuses on all aspects of machine learning accelerators for the design and development of intelligent applications and not on a single perspective such as only on reconfigurable architectures for IoT applications.
• It is the best solution for researchers to understand how to design and develop various AI, deep learning, and machine learning applications on the FPGA platform.
• It is the best solution for all types of learners to get complete knowledge of why reconfigurable architectures are important for implementing AI-ML applications with heavy computations.

Audience

Researchers, industrial experts, scientists, and postgraduate students who are working in the fields of computer engineering, electronics, and electrical engineering, especially those specializing in VLSI and embedded systems, FPGA, artificial intelligence, Internet of Things, and related multidisciplinary projects.

Table of contents

• Cover
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Strategic Infrastructural Developments to Reinforce Reconfigurable Computing for Indigenous AI Applications
• Chapter 2 Review of Artificial Intelligence Applications and Architectures
• Chapter 3 An Organized Literature Review on Various Cubic Root Algorithmic Practices for Developing Efficient VLSI Computing System—Understanding Complexity
• Chapter 4 An Overview of the Hierarchical Temporal Memory Accelerators
• Chapter 5 NLP-Based AI-Powered Sanskrit Voice Bot
• Chapter 6 Automated Attendance Using Face Recognition
• Chapter 7 A Smart System for Obstacle Detection to Assist Visually Impaired in Navigating Autonomously Using Machine Learning Approach
• Chapter 8 Crop Disease Detection Accelerated by GPU
• Chapter 9 A Relative Study on Object and Lane Detection
• Chapter 10 FPGA-Based Automatic Speech Emotion Recognition Using Deep Learning Algorithm
• Chapter 11 Hardware Implementation of RNN Using FPGA
• Index
• EULA