book

FPGA-based Implementation of Signal Processing Systems, 2nd Edition

by Roger Woods, John McAllister, Gaye Lightbody, Ying Yi

May 2017

Intermediate to advanced

360 pages

11h 42m

English

Wiley

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1.1 Introduction1.2 Field Programmable Gate Arrays1.3 Influence of Programmability1.4 Challenges of FPGAsBibliography
2.1 Introduction2.2 Definition of DSP Systems2.3 DSP Transformations2.4 Filters2.5 Adaptive Filtering2.6 Final CommentsBibliography
3.1 Introduction3.2 Number Representations3.3 Arithmetic Operations3.4 Alternative Number Representations3.5 Division3.6 Square Root3.7 Fixed-Point versus Floating-Point3.8 ConclusionsBibliography
4.1 Introduction4.2 Implications of Technology Scaling4.3 Architecture and Programmability4.4 DSP Functionality Characteristics4.5 Microprocessors4.6 DSP Processors4.7 Graphical Processing Units4.8 System-on-Chip Solutions4.9 Heterogeneous Computing Platforms4.10 ConclusionsBibliography
5.1 Introduction5.2 Toward FPGAs5.3 Altera Stratix® V and 10 FPGA Family5.4 Xilinx UltrascaleTM/Virtex-7 FPGA families 5.5 Xilinx Zynq FPGA Family5.6 Lattice iCE40isp FPGA Family5.7 MicroSemi RTG4 FPGA Family5.8 Design Stratregies for FPGA-based DSP Systems5.9 ConclusionsBibliography
6.1 Introduction6.2 FPGA Functionality6.3 Mapping to LUT-Based FPGA Technology6.4 Fixed-Coefficient DSP6.5 Distributed Arithmetic6.6 Reduced-Coefficient Multiplier6.7 ConclusionsBibliography
7.1 Introduction7.2 High-Level Synthesis7.3 Xilinx Vivado7.4 Control Logic Extraction Phase Example7.5 Altera SDK for OpenCL7.6 Other HLS Tools7.7 ConclusionsBibliography
8.1 Introduction8.2 DSP Algorithm Characteristics8.3 DSP Algorithm Representations8.4 Pipelining DSP Systems8.5 Parallel Operation8.6 ConclusionsBibliography

9.1 Introduction9.2 Motivation for Design for Reuse9.3 Intellectual Property Cores9.4 Evolution of IP cores9.5 Parameterizable (Soft) IP Cores9.6 IP Core Integration9.7 Current FPGA-based IP cores9.8 Watermarking IP9.9 SummaryBibliography
10.1 Introduction10.2 Dataflow Modeling of DSP Systems10.3 Architectural Synthesis of Custom Circuit Accelerators from DFGs10.4 Model-Based Development of Multi-Channel Dataflow Accelerators10.5 Model-Based Development for Memory-Intensive Accelerators10.6 SummaryNotesBibliography
11.1 Introduction to Adaptive Beamforming11.2 Generic Design Process11.3 Algorithm to Architecture11.4 Efficient Architecture Design11.5 Generic QR Architecture11.6 Retiming the Generic Architecture11.7 Parameterizable QR Architecture11.8 Generic Control11.9 Beamformer Design Example11.10 SummaryBibliography
12.1 Introduction12.2 Big Data12.3 Big Data Analytics12.4 Acceleration12.5 k-Means Clustering FPGA Implementation12.6 FPGA-Based Soft Processors12.7 System Hardware12.8 ConclusionsBibliography
13.1 Introduction13.2 Sources of Power Consumption13.3 FPGA Power Consumption13.4 Power Consumption Reduction Techniques13.5 Dynamic Voltage Scaling in FPGAs13.6 Reduction in Switched Capacitance13.7 Final CommentsBibliography
14.1 Introduction14.2 Evolution in FPGA Design Approaches14.3 Big Data and the Shift toward Computing14.4 Programming Flow for FPGAs14.5 Support for Floating-Point Arithmetic14.6 Memory ArchitecturesBibliography

Content preview from FPGA-based Implementation of Signal Processing Systems, 2nd Edition

14 Conclusions

14.1 Introduction

The aim of this book has been to cover many of the techniques needed to create FPGA solutions for signal and data processing systems. Interest in FPGAs for such systems has grown since the first edition as vendors have targeted this market and introduced innovations into their technology offerings to make them more attractive. This has included dedicated DSP blocks typically comprising 18-bit MAC blocks and increased memory units but also increased parallelism in terms of resources and the ability to employ pipelining to improve speed of operation. To support this activity, FPGA companies have created a range of IP cores (for customers to use in their designs) and design tools to create such implementations.

The main attraction of using FPGAs is that the resulting designs provide a very high quality of performance, particularly if MSPS/mm²/W is considered. This is possible as the designer is able to create an architecture which is a good match to the algorithmic needs, rather than striving to map the requirements onto a fixed architecture as is the case in microcontrollers, DSP microprocessors or GPUs, even though vendors increasingly offer multicore platforms. The main design challenge is to create a suitable architecture for the algorithmic requirements.

The creation of this suitable FPGA architecture comes from employing the right level of parallelism and pipelining to match initially the throughput rate and then area and power consumption ...