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

13 Low-Power FPGA Implementation

13.1 Introduction

A key trend in the introduction was technology scaling and how increasing power consumption has become a worrying aspect of modern computing design. As was highlighted in Chapter 1, this has led to serious interest by major computing companies such as Intel, IBM and Microsoft in exploiting FPGA technology. Whilst in some cases FPGA implementations may offer only moderate computational improvement over CPU/GPU implementations, the equivalent designs tend to operate at much lower clock rates and power is directly proportional to this factor.

Power consumption scales down with technology evolution, and so for much of the 1980s and 1990s the new technology evolution offered an increased number of transistors operating not only at increased speed, but also at reduced power consumption. As scaling increased, though, leakage power, caused by the increasingly imperfect performance of the gate oxide thickness, increased. As the gate leakage is inversely proportional to the gate oxide thickness, this became and continues to be an increasingly important problem.

Even though some would argue to the contrary, the switch to FPGAs could be considered to be the low-power solution for high-performance computing companies, and there are a number of important reasons to reducing FPGA power consumption. As power consumption is directly related to increased temperature, improved FPGA implementations have immediate benefits for the design of the ...