The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field.

· Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes
· Views of FPGA programming beyond Verilog/VHDL
· Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

Steady advances in VLSI technology and design tools have extensively expanded the application domain of digital signal processing over the past decade. While application-specific integrated circuits (ASICs) and programmable digital signal processors (PDSPs) remain the implementation mechanisms of choice for many DSP applications, increasingly new system implementations based on reconfigurable computing are being considered. These flexible platforms, which offer the functional efficiency of hardware and the programmability of software, are quickly maturing as the logic capacity of programmable devices follows Moore's Law and advanced automated design techniques become available. As initial reconfigurable technologies have emerged, new academic and commercial efforts have been initiated to support power optimization, cost reduction, and enhanced run-time performance.

This paper presents a survey of academic research and commercial development in reconfigurable computing for DSP systems over the past fifteen years. This work is placed in the context of other available DSP implementation media including ASICs and PDSPs to fully document the range of design choices available to system engineers. It is shown that while contemporary reconfigurable computing can be applied to a variety of DSP applications including video, audio, speech, and control, much work remains to realize its full potential. While individual implementations of PDSP, ASIC, and reconfigurable resources each offer distinct advantages, it is likely that integrated combinations of these technologies will provide more complete solutions.

http://www.umiacs.umd.edu/~joseph/classes/enee640/fpga-dsp-survey.pdf

Reconfigurable Computing for Digital Signal Processing: A Survey

Moore's Law states that the number of transistors on a device doubles every two years; however, it is often (mis)quoted based on its impact on CPU performance. This important corollary of Moore's Law states that improved clock frequency plus improved architecture yields a doubling of CPU performance every 18 months. This paper examines the impact of Moore's Law on the peak floating-point performance of FPGAs. Performance trends for individual operations are analyzed as well as the performance trend of a common instruction mix (multiply accumulate). The important result is that peak FPGA floating-point performance is growing significantly faster than peak floating-point performance for a CPU.

/pdf/fpgas-vs-cpus-trends-in-peak-floating-point-performance-w1q6p0ta0k.pdf

FPGAs vs. CPUs: trends in peak floating-point performance

Field programmable gate arrays (FPGAs) have long been an attractive alternative to microprocessors for computing tasks - as long as floating-point arithmetic is not required. Fueled by the advance of Moore's law, FPGAs are rapidly reaching sufficient densities to enhance peak floating-point performance as well. The question, however, is how much of this peak performance can be sustained. This paper examines three of the basic linear algebra subroutine (BLAS) functions: vector dot product, matrix-vector multiply, and matrix multiply. A comparison of microprocessors, FPGAs, and reconfigurable computing platforms is performed for each operation. The analysis highlights the amount of memory bandwidth and internal storage needed to sustain peak performance with FPGAs. This analysis considers the historical context of the last six years and is extrapolated for the next six years.

/pdf/closing-the-gap-cpu-and-fpga-trends-in-sustainable-floating-14np1obgbi.pdf

Closing the gap: CPU and FPGA trends in sustainable floating-point BLAS performance

The use of reconfigurable hardware to perform high precision operations such as IEEE floating point operations has been limited in the past by FPGA resources. We discuss the implementation of IEEE single precision floating-point multiplication and addition. Then, we assess the practical implications of using these operations in the Xilinx 4000 series FPGAs considering densities available now and scheduled for the near future. For each operation, we present space requirements and performance information. This is followed by a discussion of an algorithm, matrix multiplication, based on these operations, which achieves performance comparable to conventional microprocessors. Algorithm implementation options and their performance implications are discussed and corresponding measured results are given.

A re-evaluation of the practicality of floating-point operations on FPGAs

Floating point operations are hard to implement on FPGAs because of the complexity of their algorithms. On the other hand, many scientific problems require floating point arithmetic with high levels of accuracy in their calculations. Therefore, we have explored FPGA implementations of addition and multiplication for IEEE single precision floating-point numbers. Customizations were performed where this was possible in order to save chip area, or get the most out of our prototype board. The implementations tradeoff area and speed for accuracy. The adder is a bit-parallel adder, and the multiplier is a digit-serial multiplier. Prototypes have been implemented on Altera FLEX8000s, and peak rates of 7 MFlops for 32-bit addition and 2.3 MFlops for 32-bit multiplication have been obtained.

Louca

Papers

Implementation of IEEE single precision floating point addition and multiplication on FPGAs