Application development for heterogeneous platforms requires to code and map functionalities on a set of different computing elements. As a consequence, the development process needs a clear understanding of both, application requirements and heterogeneous computing technologies. To support the development process, we propose a framework called DRuiD capable of learning application characteristics that make them suitable for certain computing elements. The framework is composed of an expert system that supports the designer in the mapping decision and gives hints on possible code modifications to be applied to make the functionality more suitable for a computing element. The experimental results are tailored for a heterogeneous and reconfigurable platform (the Xilinx-ml510) including two computational elements, i.e. a Virtex5 FPGA and a PowerPC. The expert system identifies 88.9% of the times what are the functionalities that are accelerated efficiently by using the FPGA, without requiring the kernel porting. Additionally, we present two case studies demonstrating the potentialities of the framework to give hints on high level code modifications for an efficient kernel mapping on the FPGA.

There has been a steady increase in the utilization of heterogeneous architectures to tackle the growing need for computing performance and low-power systems. The execution of computation-intensive functions on specialized hardware enables to achieve substantial speedups and power savings. However, with a large legacy code base and software engineering experts, it is not at all obvious how to easily utilize these new architectures. As a result, there is a need for comprehensive tool support to bridge the knowledge gap of many engineers as well as to retarget legacy code. In this article, we present the Quipu modeling approach, which consists of a set of tools and a modeling methodology that can generate hardware estimation models, which provide valuable information for developers. This information helps to focus their efforts, to partition their application, and to select the right heterogeneous components. We present Quipu ’s capability to generate domain-specific models, that are up to several times more accurate within their particular domain (error: 4.6%) as compared to domain-agnostic models (error: 23%). Finally, we show how Quipu can generate models for a new toolchain and platform within a few days.

In the last decade, a considerable amount of effort was spent on raising the implementation level of hardware systems by automatically extracting the parallelism from input applications and using tools to generate Hardware/Software co-design solutions. However, the tools developed thus far either focus on particular application domains or they impose severe restrictions on the input language. In this paper, we present the DWARV 2.0 compiler that accepts general C-code as input and generates synthesizable VHDL for unrestricted application domains. Dissimilar to previous hardware compilers, this implementation is based on CoSy compiler framework. This allowed us to build a highly modular compiler in which standard or custom optimizations can be easily integrated. Validation experiments showed speed-ups of up to 4.41× when comparing against another state of the art hardware compiler.

In recent years, multicore systems have become a dominant architecture, introducing new challenges that need to be addressed in order to take full advantage of their efficiency. Reconfigurable computing has also received a great deal of attention due to its ability to increase the performance of an application through hardware execution, while retaining the flexibility of a software solution. Grouping tasks within an application contributes to coarse-grained partitioning, which can eventually improve the performance of the system. In this paper, we introduce a clustering framework along with a flexible multi-purpose clustering algorithm that initiates task clustering at the functional level based on dynamic profiling information. The clustering framework can be used as the basic step to modify the granularity of tasks in the hardware/software partitioning and scheduling phases. As a result, an elaborate mapping onto the system resources and possibly a higher degree of task parallelism becomes feasible. The framework particularly targets two objectives, 1) to form workload-balanced and 2) loosely-coupled clusters. We evaluated its efficiency using MJPEG as a case study. The experimental results comply with the desired clustering metrics defined through the objectives.

In this paper, two general algorithms for the automatic generation of instruction-set extensions are presented. The basic instruction set of a reconfigurable architecture is specialized with new application-specific instructions. The paper proposes two methods for the generation of convex multiple input multiple output instructions, under hardware resource constraints, based on a two-step clustering process. Initially, the application is partitioned in single-output instructions of variable size and then, selected clusters are combined in convex multiple output clusters following different policies. Our results on well-known kernels show that the extended instructions-set allows to execute applications more efficiently and needing fewer cycles. Our results show that a significant overall application speed-up is achieved even for large kernels (for ADPCM decoder the speed-up is up to x2.2 and for TWOFISH encoder the speedup is up to x5.5).

Recently, reconfigurable computing has received a great deal of attention due to its ability to increase an application performance with hardware execution, while possessing the flexibility of software solution. One of the major requirements for such systems is to identify which application or part of the application can be implemented as software and which can be mapped onto reconfigurable devices. Grouping the tasks within an application can intensify coarse-grained partitioning of the application, which can eventually improve the performance of the system. In this work, we introduce a clustering framework along with a flexible multipurpose clustering algorithm that initiates task clustering at the functional level based on dynamic profiling information. The clustering framework can be used as the basic step to modify the granularity of tasks in the hardware/software partitioning and scheduling phases. As a result, an elaborate mapping onto the system resources and possibly a higher degree of task parallelism can be obtained. In an initial attempt, the framework addresses two primary objectives to create workload-balanced and loosely-coupled clusters. The experimental results show that the clustering complies with the desired metrics, which were defined through the objectives.

In this paper, we present an approach for prediction of interconnect resources at the early stages of design. This approach was developed as an extension to the Quipu multi-dimensional quantitative prediction model for early design space exploration. Quipu is a part of the Delft Workbench project, a semi-automatic tool platform supporting integrated hardware-software co-design for heterogeneous computing systems. Because of the highly iterative nature of design in such tool platforms, fast and early estimates of hardware properties are required. One aspect of particular importance is the utilization of interconnect resources, which has increased with designs becoming larger, even to the point where some designs are no longer routable. We establish a method of estimating interconnect from a C-level description using partial least squares regression (PLSR) and software complexity metrics (SCM) for use in the Delft Workbench tool platform. We show that our approach can make predictions with an expected error of 31.6%.

With the arrival of partial reconfiguration technology, modern FPGAs support swapping tasks in or out individually at run-time without interrupting other tasks running on the same FPGA. Although, implementing this feature achieves much better flexibility and device utilization, the challenge remains to quickly and efficiently place tasks arriving at run-time on such partially reconfigurable systems. In this paper, we propose an algorithm to handle this on-line, run-time task placement problem. By adding logical constraints on the FPGA and introducing our resources management solution, the simulation results show our algorithm has better overall performances compared with previous reported methods in terms of task rejection number, placement quality and execution time.

The aim of the hArtes project is to facilitate and automate the rapid design and development of heterogeneous embedded systems, targeting a combination of a general purpose embedded processor, digital signal processing and reconfigurable hardware. In this paper, we evaluate three tools from the hArtes toolchain supporting profiling, compilation, and HDL generation. These tools facilitate the HW/SW partitioning, co-design, co-verification, and co-execution of demanding embedded applications. The described tools are provided by the DelftWorkBench framework1. Experimental results on MJPEG and G721 encoder application case studies suggest overall performance improvement of 228% and 36% respectively.

An important step in Heterogeneous System Development is Hardware/Software Partitioning. This process involves exploring a huge design space. By using profiling to select hot-spots and estimate area and delay we can prune the design space considerably. We present a Quantitative Model that makes early predictions to prune the design space and support the partitioning process. The model is based on Software Complexity Metrics, which capture important aspects of functions as control intensity, data intensity, and code size. To remedy interdependence among software metrics, we performed a Principal Component Analysis. The hardware characteristics were determined by automatically generating VHDL from C using the DWARV C-to-VHDL compiler. Linear regression on these data generated our model. The model error differs per hardware characteristic. We show that for flip-flops the mean error is 69%. In conclusion, our quantitative model makes fast and sufficiently accurate area predictions in support of early Hardware/Software Partitioning.

Reconfigurable computing (RC) systems, coupling general purpose processor with reconfigurable components, offer a lot of advantages. Nevertheless, currently a designer needs both in-depth software and hardware design knowledge to develop applications for such platforms. The automated hardware generation addresses this problem. However, the success of such tools remains marginal. This paper discusses the reasons for the lack of success. It presents a quantitative and qualitative comparison of three hardware generators using the following criteria: quality of the hardware model, the supported HLL constructs, and the level of automation.