A practical guide to the use of FPGAs for deep learning and its real-world applications in signal processing

In Deep Learning Meets FPGA, a team of distinguished researchers delivers an expert discussion on how to use field programmable gate arrays (FPGAs) to apply deep learning techniques to signal processing. The book explains why technologists may decide to forego the traditional methods of using CPU and GPU architectures so they can access the improved processing speed, flexibility, and efficiency of FPGA technology.

The book discusses FPGA architecture, optimization techniques, toolchains, and frameworks for FPGA development. It covers the implementation of convolutional neural networks, recurrent neural networks, and real-time processing applications. The information is accompanied by example use cases in audio and video signal processing, as well as strategies for power-efficient FPGA designs.

Readers will also find:

• A thorough introduction to the challenges and obstacles posed by traditional approaches to deep learning applications in signal processing and how those can be solved using FPGAs
• Comprehensive explorations of deep learning applications in sensor data integration
• Practical discussions of up-to-date debugging and validation techniques using FPGA designs
• Cutting-edge explorations of potential future trends and promising areas of research for further development of FPGAs

Perfect for computer science researchers and postgraduate students interested in signal processing, Deep Learning Meets FPGA will also benefit practicing signal processing engineers.

Jyotirmoy Pathak, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at Christ University in Bangalore, India. He has published over twenty research papers in Scopus/WoS-indexed journals and presented at IEEE/Springer conferences. His research expertise include side channel attack, VLSI design, low power architecture, memory design, and more.

Abhishek Kumar, PhD, is an Associate Professor at the School of Electronics and Electrical Engineering, Lovely Professional University. His research expertise focus on VLSI design, low power CMOS circuits, hardware security, and more. He has authored multiple books, patents, and over 30 research papers in reputed SCI and Scopus-indexed journals.

Jyoti Kandpal, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering, Graphic Era Hill University, Dehradun, India. Her research interests focus on VLSI design and low-power CMOS circuit optimization. She has published numerous scholarly articles in esteemed SCI and Scopus-indexed journals.