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

6 Detailed FPGA Implementation Techniques

6.1 Introduction

Chapter 4 highlighted the wide range of technologies available for implementing DSP systems and Chapter 5 described the various FPGA offerings. The remaining chapters now describe the issues for implementing complex DSP systems onto FPGA-based heterogeneous platforms and also a single FPGA device! This encompasses considerations such as selection of a suitable model for DSP system implementation, partitioning of DSP complexity into hardware and software, mapping of DSP functions efficiently onto FPGA hardware, development of a suitable memory architecture, and achievement of design targets in terms of throughput, area and energy. However, it is imperative that the reader understands the detailed FPGA implementation of DSP functionality in order that this process is inferred correctly at both the system partitioning and circuit architecture development stages.

A key advantage of FPGAs is that the user can develop highly parallel, pipelined circuitry which can offer very high levels of performance. If these individual circuits can be optimized to exploit specific features of the underlying hardware, then this will offer increased speed or allow more processing functionality, or even both. This comes from efficiently mapping data paths or, in some cases, the controlling circuitry which controls the flow of data, into FPGA hardware. Controllers are usually mapped into finite state machines (FSMs) or implemented as software ...