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.