Fpga Based Digital Convolution For Wireless Applications


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FPGA-based Digital Convolution for Wireless Applications


FPGA-based Digital Convolution for Wireless Applications

Author: Lei Guan

language: en

Publisher: Springer

Release Date: 2017-01-16


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This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear convolution in digital pre-distortion (DPD) enabled high-efficiency wireless RF transceivers; and fast linear convolution in massive multiple-input multiple-output (MIMO) systems. After reading this book, students and professionals will be able to: · Understand digital convolution with inside-out information: discover what convolution is, why it is important and how it works. · Enhance their FPGA design skills, i.e., enhance their FPGA-related prototyping capability with model-based hands-on examples. · Rapidly expand their digital signal processing (DSP) blocks: to examine how to rapidly and efficiently create (DSP) functional blocks on a programmable FPGA chip as a reusable intellectual property (IP) core. · Upgrade their expertise as both “thinkers” and “doers”: minimize/close the gap between mathematical equations and FPGA implementations for existing and emerging wireless applications.

Towards Ubiquitous Low-power Image Processing Platforms


Towards Ubiquitous Low-power Image Processing Platforms

Author: Magnus Jahre

language: en

Publisher: Springer Nature

Release Date: 2020-12-15


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This book summarizes the key scientific outcomes of the Horizon 2020 research project TULIPP: Towards Ubiquitous Low-power Image Processing Platforms. The main focus lies on the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications. The holistic TULIPP approach is described in the book, which addresses hardware platforms, programming tools and embedded operating systems. Several of the results are available as open-source hardware/software for the community. The results are evaluated with several use cases taken from real-world applications in key domains such as Unmanned Aerial Vehicles (UAVs), robotics, space and medicine. Discusses the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications; Covers the hardware architecture of embedded image processing systems, novel methods, tools and libraries for programming those systems as well as embedded operating systems to manage those systems; Demonstrates results with several challenging applications, such as medical systems, robotics, drones and automotive.

Optics for AI and AI for Optics


Optics for AI and AI for Optics

Author: Jinlong Wei

language: en

Publisher: MDPI

Release Date: 2020-06-23


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Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields.