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Christoph Kessler

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Other high-level synthesis approaches try to hide the complexity of hardware clock cycles, data movement, concurrency, etc. Instead, BSV exposes it to the user as an intuitive high-level metaphor. This language is a good candidate for expert hardware designers with a background on Register-Transfer Level RTL languages, such as Verilog or VHDL, for designers that have to develop critical hardware components, or for keeping a very tight control over the performance and the resources used.

This chapter introduces the basic concepts of Bluespec SystemVerilog. LegUp is a High-level Synthesis tool under active development at the University of Toronto since The tool is on its fourth public release, is open source and freely downloadable. LegUp has been the subject of over 15 publications and has been downloaded by over groups from around the world. In this section, we overview LegUp, its programming model, unique aspects of the tool versus other HLS offerings, and conclude with a case study. To put in a historical context: Field programmable gate arrays FPGAs were much smaller, and slower, then they are today ; Graphics processing units GPUs were used exclusively for graphics ; reconfigurable computing was taking shape as a research area but not yet within the main stream of academic research, let alone in industrial production.

However, multiple research projects had already demonstrated, many times over, the clear advantages and potentials of this nascent paradigm as an alternative that combines the re-programmability advantages of fixed data path devices Central processing units CPUs , Digital signal processors DSPs and GPUs with the high speed of custom hardware Application-specific integrated circuits ASICs.

Within that time frame, the nearly exclusive focus of reconfigurable computing was on signal and image processing because of their streaming nature. Video processing was considered a future possibility to be realized when the size area and bandwidth capabilities of FPGAs got larger. To ease the burden on developers, Domain-specific languages DSLs aim at combining architecture- and domain-specific knowledge, thereby delivering performance, productivity, and portability.

HIPAcc is a publicly available framework for the automatic code generation of image processing algorithms on Graphics processing unit GPU accelerators. In this chapter, we present an introduction to the ReconOS operating system for reconfigurable computing. ReconOS offers a unified multi-threaded programming model and operating system services for threads executing in software and threads mapped to reconfigurable hardware.

By supporting standard POSIX operating system functions for both software and hardware threads, ReconOS particularly caters to developers with a software background, because developers can use well-known mechanisms such as semaphores, mutexes, condition variables, and message queues for developing hybrid applications with threads running on the CPU and FPGA concurrently.

Through the semantic integration of hardware accelerators into a standard operating system environment, ReconOS allows for rapid design space exploration, supports a structured application development process and improves the portability of applications between different reconfigurable computing systems.

FPGAs offer attractive power and performance for many applications, especially relative to traditional sequential architectures. In spite of these advantages, FPGAs have been deployed in only a few, niche domains. We argue that the difficulty of programming FPGAs all but precludes their use in more general systems: FPGA programmers are currently exposed to all the gory system details that software operating systems long ago abstracted away. LEAP addresses the FPGA programming problem by providing a rich set of portable latency-insensitive abstraction layers for program development.

Unlike software operating systems services, which are generally dynamic, the nature of FPGAs requires that many configuration decisions be made at compile time.

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  7. We present an extensible interface for compile-time management of resources. This chapter provides an overview of Systems-on-Chip SoC implemented on reconfigurable technology. FPGA vendors provide SoC design tools, which allow for rapid development of such systems by combining together different intellectual property cores into a customized hardware system, capable of executing user-provided software. Using an FPGA to implement an SoC provides software designers a fabless methodology to create and tailor hardware systems for their specific software workloads.

    This chapter describes the advantages and limitations of designing SoC on reconfigurable technology, what is possible with modern FPGA vendor SoC design tools, and the main steps in creating such systems. As such, this chapter intentionally does not contain step-by-step instructions; instead, it focuses on the overarching concepts and techniques used in the latest SoC tools. Jeffrey Goeders, Graham M. Holland, Lesley Shannon, Steven J. Developing applications that run on FPGAs is without doubt a very different experience from writing programs in software.

    Not only is the hardware design process fundamentally different from that of software development, software programmers also often find themselves constantly battling with the much lower design productivity in developing hardware designs. In this chapter, we explore how the concept of FPGA overlay may be able to alleviate some of these burdens.

    We will look at how by using an overlay architecture, designers are able to compile applications to FPGA hardware in merely seconds instead of hours. We will also look at how overlays are able to help with design portability, as well as to improve debugging capabilities of low-level designs.

    FPGAs for Software Programmers

    Finally, we will explore the challenges and opportunities for future research in this area. In this context, powerful and robust tools are needed in order to accomplish the transition from code-based programming to model-based programming. In this paper we propose a novel approach and tools where system-level models are compiled into standard C code while optimizing the system's memory footprint. From the compiled C code, we generate both a software implementation for a Digital Signal Processor platform and a hardware-software implementation for a platform based on hardware Intellectual Property IP blocks.

    Our optimizations achieve a memory footprint reduction of The complexity of today's multi-processor architectures raises the need to increase the level of abstraction of software development paradigms above third-generation programming languages e. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require Electronic System-Level ESL information on both the algorithms and the architecture.

    Contrary to algorithm models for which a major body of work has been conducted on defining formal Models of Computation MoCs , architecture models from the literature are mostly empirical models from which reproducible experimentation requires the accompanying software. In this paper, a precise definition of a Model of Architecture MoA is proposed that focuses on reproducibility and abstraction and removes the overlap previously existing between the notions of MoA and MoC.

    To demonstrate the generic nature of the proposed new architecture modeling concepts, we show that the LSLA Model can be integrated flexibly with different MoCs. A method to automatically learn LSLA model parameters from platform measurements is introduced. Current trends in high performance and embedded computing include design of increasingly complex hardware architectures with high parallelism, heterogeneous processing elements and non-uniform communication resources. This study focuses on porting hyperspectral image processing into manycore platforms by optimizing their processing to fulfill real-time constraints, fixed by the image capture rate of the hyperspectral sensor.

    Real-time is a challenging objective for hyperspectral image processing, as hyperspectral images consist of extremely large volumes of data and this problem is often solved by reducing image size before starting the processing itself.

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    To tackle the challenge, this paper proposes an analysis of the intrinsic parallelism of the different stages of the PCA algorithm with the objective of exploiting the parallelization possibilities offered by an MPPA manycore architecture. Furthermore, the impact on internal communication when increasing the level of parallelism is also analyzed. Experimenting with medical images obtained from two different surgical use cases, an average speedup of 20 is achieved. Internal communications are shown to rapidly become the bottleneck that reduces the achievable speedup offered by the PCA parallelization.

    DSP for Embedded and Real-Time Systems [Book]

    As a result of this study, PCA processing time is reduced to less than 6s, a time compatible with the targeted brain surgery application requiring 1frame-per-minute. Approximate computing can be applied at different levels of abstraction, from algorithm level to application level. Approximate computing at algorithm level reduces the computational complexity by approximating or skipping computational blocks.

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    A number of applications in the signal and image processing domain integrate algorithms based on discrete optimization techniques. These techniques minimize a cost function by exploring an application parameter search space. In this paper, a new methodology is proposed that exploits the computation-skipping approximate computing concept.

    The methodology, named Smart Search Space Reduction Sssr , explores at design time the Pareto relationship between computational complexity and application quality. At run time, an approximation manager can then early select a good candidate configuration. Sssr reduces the run time search space and, in turn, reduces computational complexity. An efficient Sssr technique adjusts at design time the configuration selectivity while selecting at run time the most suitable functions to skip.


    In this application, two discrete optimizations are performed. They explore different coding parameters and select the values leading to the minimal cost in terms of a tradeoff between bitrate, quality and computational energy by acting on both the Hevc coding-tree partitioning and the intra-modes. The approximate computing paradigm provides methods to optimize algorithms while considering both application quality of service and computational complexity.

    Consequently, energy consumption becomes a key criterion to take into consideration during Design Space Exploration DSE. Finding a trade-off between energy consumption and performance early in the design flow in order to satisfy time-to-market is a design challenge of EDA tools.

    The key contribution of the proposed framework is the implementation of an energy-aware scheduling process, named PreesmPE , that combines state-of-the-art power management techniques together with Clustering-based Scheduling. To demonstrate the efficiency of the proposed approach, we conducted experiments using the H. The obtained results demonstrate that the energy-aware scheduling process can effectively save energy in MP2SoC systems. They also confirmed that our MDE-based approach accelerates the DSE process while generating energy-efficient design decisions.

    In recent years, the Electronic Design Automation EDA community shifted spotlights from performance to energy efficiency. However, it is essential to remember that UML is unable to solve the difficulty associated with embedded systems analysis, but it only provides standard modeling means. A reliable Design Space Exploration DSE process which suits the peculiarities of complex embedded systems design is necessary to complement the use of UML for design space exploration.