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

A one-of-a-kind survey of the field of Reconfigurable Computing Gives a comprehensive introduction to a discipline that offers a 10X-100X acceleration of algorithms over microprocessors Discusses the impact of reconfigurable hardware on a wide range of applications: signal and image processing, network security, bioinformatics, and supercomputing Includes the history of the field as well as recent advances Includes an extensive bibliography of primary sources

Reconfigurable Computing: Accelerating Computation with Field-Programmable Gate Arrays

Modern development methods and tools for embedded reconfigurable systems: A survey

Acceleration in the form of customized datapaths offer large performance and energy improvements over general purpose processors. Reconfigurable fabrics such as FPGAs are gaining popularity for use in implementing application-specific accelerators, thereby increasing the importance of having good high-level FPGA design tools. However, current tools for targeting FPGAs offer inadequate support for high-level programming, resource estimation, and rapid and automatic design space exploration.We describe a design framework that addresses these challenges. We introduce a new representation of hardware using parameterized templates that captures locality and parallelism information at multiple levels of nesting. This representation is designed to be automatically generated from high-level languages based on parallel patterns. We describe a hybrid area estimation technique which uses template-level models and design-level artificial neural networks to account for effects from hardware place-and-route tools, including routing overheads, register and block RAM duplication, and LUT packing. Our runtime estimation accounts for off-chip memory accesses. We use our estimation capabilities to rapidly explore a large space of designs across tile sizes, parallelization factors, and optional coarse-grained pipelining, all at multiple loop levels. We show that estimates average 4.8% error for logic resources, 6.1% error for runtimes, and are 279 to 6533 times faster than a commercial high-level synthesis tool. We compare the best-performing designs to optimized CPU code running on a server-grade 6 core processor and show speedups of up to 16.7×.

Automatic generation of efficient accelerators for reconfigurable hardware

Early performance feedback and design space exploration of complete field-programmable gate array (FPGA) designs are still time consuming tasks. This paper proposes an original methodology based on estimations to reduce the impact on design time. It promotes a hierarchical exploration to mitigate the complexity of the exploration process. Therefore, this work takes place before any design step, such as compilation or behavioral synthesis, where the specification is still provided as a C program. The goal is to provide early area and delay evaluations of many register-transfer level (RTL) implementations to prune the design space. Two main steps compose the flow: 1) a structural exploration step defines several RTL implementations, and 2) a physical mapping estimation step computes the mapping characteristics of these onto a given FPGA device. For the structural exploration, a simple yet realistic RTL model reduces the complexity and permits a fast definition of solutions. At this stage, it focuses on the computation parallelism and memory bandwidth. Advanced optimizations using for instance loop tiling, scalar replacement, or data layout are not considered. For the physical estimations, an analytical approach is used to provide fast and accurate area/delay tradeoffs. The paper also do not consider the impact of routing on critical paths or other optimizations. The reduction of the complexity allows the evaluation of key design alternatives, namely target device and parallelism that can also include the effect of resource allocation, bitwidth, or clock period. Due to this, a designer can quickly identify a reliable subset of solutions for which further refinement can be applied to enhance the relevance of the final architecture and reach a better use of FPGA resources, i.e., an optimal level of performance. Experiments performed with Xilinx (VirtexE) and Altera (Apex20K) FPGAs for a two-dimensional Discrete Wavelet Transform and a G722 speech coder lead to an average error of 10% for temporal values and 18% for area estimations

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Design Space Pruning Through Early Estimations of Area/Delay Tradeoffs for FPGA Implementations

Field-programmable gate arrays (FPGAs) have become increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach and to compare it with traditional alternatives.

In this paper, we propose a high-level estimation methodology for area and performance parameters of regular FPGA designs to be found in multimedia, telecommunications or cryptography. The goal is to provide a means that allows early quantification of an FPGA design and that enables early trade-off considerations. We present our estimation approach as well as evaluation results, which are based on several implemented applications and prove the suitability of the proposed estimation approach.

Tobias Jeger

Papers

High-Level Area and Performance Estimation of Hardware Building Blocks on FPGAs

The performance prediction model