Includes bibliographical references and index.

Strategic Infrastructural Developments to Reinforce Reconfigurable Computing for Indigenous AI Applications / Deepti Khurge -- Review of Artificial Intelligence Applications and Architectures / Rashmi Mahajan, Dipti Sakhare, Rohini Gadgil -- An Organized Literature Review on Various Cubic Root Algorithmic Practices for Developing Efficient VLSI Computing System-Understanding Complexity / Siba Kumar Panda, Konasagar Achyut, Swati K Kulkarni, Akshata A Raut, Aayush Nayak -- An Overview of the Hierarchical Temporal Memory Accelerators / Abdullah M Zyarah, Dhireesha Kudithipudi -- NLP-Based AI-Powered Sanskrit Voice Bot / Vedika Srivastava, Arti Khaparde, Akshit Kothari, Vaidehi Deshmukh -- Automated Attendance Using Face Recognition / Kapil Tajane, Vinit Hande, Rohan Nagapure, Rohan Patil, Rushabh Porwal -- A Smart System for Obstacle Detection to Assist Visually Impaired in Navigating Autonomously Using Machine Learning Approach / Vijay Dabhade, Dnyaneshwar Dhawalshankh, Anuradha Thakare, Maithili Kulkarni, Priyanka Ambekar -- Crop Disease Detection Accelerated by GPU / Abhishek Chavan, Anuradha Thakare, Tulsi Chopade, Jessica Fernandes, Omkar Gawari -- A Relative Study on Object and Lane Detection / Rakshit Jha, Shruti Sonune, Mohammad Taha Shahid, Santwana Gudadhe -- FPGA-Based Automatic Speech Emotion Recognition Using Deep Learning Algorithm / Rupali Kawade, Triveni Dhamale, Dipali Dhake -- Hardware Implementation of RNN Using FPGA / Nikhil Bhosale, Sayali Battuwar, Gunjan Agrawal, SD Nagarale.

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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.

Provider: Wiley.