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The present era belongs to the innovations related to artificial intelligence (AI) and machine learning (ML). Almost all the applications nowadays use AI-ML algorithms either for prediction or accurate classification. Gradually AI-ML algorithms, having better accuracy, replacing the conventional algorithms in devices like medical instruments, defence instruments, measuring instruments etc.
Over the past few years, many AI-ML algorithms have developed. Out of these algorithms, few are suitable for offline processing and few are suitable for real time applications. The objective of the book is to discuss AI-ML algorithms which are suitable for real time applications. The criteria for an AI-ML algorithm to be fit for real time application are low computational complexity, low processing speed, and high accuracy. The second objective of this book is to implement a few of the most important algorithms on both software and hardware platforms. The algorithms can be implemented either using MATLAB or Python. But to maintain uniformity, python is selected. All the algorithms will be implemented using minimal inbuilt functions. Few selected algorithms will be implemented on a reconfigurable hardware like field programmable gate array (FPGA).
Python codes will be added in the Appendix of the book to make the book more useful to the readers. All the architectures will be discussed in detail and performance analysis of the FPGA implementations will also be provided. The Verilog codes will be added in the Appendix of the book to give readers more practical knowledge
Dr. Shirshendu Roy received his Bachelor of Engineering (B.E.) in Electronics and Tele-Communication Engineering in 2010 and Master of Engineering (M.E.) in Digital Systems and Instrumentation in 2016 from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India. He completed his Ph.D. in VLSI Signal Processing from the National Institute of Technology (NIT), Rourkela, Odisha, India. He has four years of industrial experience as a Control and Instrumentation Engineer at Hindalco Industries Limited, which strengthened his practical understanding of real-time and industrial systems. He previously served as an Assistant Professor at GIET University, Odisha, and is currently working as an Assistant Professor at Dayananda Sagar University, Bengaluru. Dr. Roy has published research articles in international journals and conferences and has also authored textbooks in the area of digital system design. His research interests include compressed sensing, FPGA-based implementation of signal, image, and machine learning algorithms, artificial neural networks, low-power architecture design, ASIC design, and FPGA-based IoT applications.
Dr. Arun Balodi is a Professor and Chairman in the Department of Electronics and Communication Engineering at Dayananda Sagar University, Bengaluru, India. He is a Senior Member of IEEE, Fellow of IETE, and Life Member of ISTE. He earned his Ph.D. from IIT Roorkee, M.Tech. in Digital Signal Processing (Gold Medalist) from GBPEC Pauri, and B.Tech. in ECE from UPTU Lucknow. With over 17 years of teaching and research experience, his interests include Biomedical Signal and Image Processing, Medical Image Analysis, Artificial Intelligence, and Machine Learning. He has published 65+ papers, holds five patents, and received multiple research excellence awards.
Dr. Jisy N K received her Ph.D. from the Department of Electrical and Electronics Engineering at BITS Pilani, Hyderabad Campus, India. Her research interests include biomedical image processing and the application of machine learning and deep learning for fundus image analysis. She is currently an Assistant Professor in the Electronics and Communication Engineering department at Dayananda Sagar University, Bengaluru. She earned her B.Tech in Electronics and Communication Engineering from AWH Engineering College, Kerala in 2009, and her Master’s degree in VLSI Design from Anna University Regional Centre, Coimbatore in 2013. She has published in reputed national and international conferences and peer-reviewed journals, holds a published patent in embedded AI for healthcare, and her current research focuses on deep learning for real-time applications and FPGA-based edge devices.
1 Mathmatical Background for Machine Learning
2 AIML Algorithms and Their Real-Time Applications
3 Basics of FPGA Implementation
4 Regression Techniques
5 Matrix Dimentionality Reduction Techniques
6 Clustering Techniques
7 Classification Algorithms
8 Activation Functions with their FPGA Implementation
9 Artificial Neural Networks
10 Convolutional Neural Network
11 Recurrent Neural Network
12 Hardware Optimization Techniques
13 Deep Neural Networks
14 Appendix 1
15 Appendix 2
