14 research outputs found

    High-level dataflow design of signal processing systems for reconfigurable and multicore heterogeneous platforms

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    The potential computational power of today multicore processors has drastically improved compared to the single processor architecture. Since the trend of increasing the processor frequency is almost over, the competition for increased performance has moved on the number of cores. Consequently, the fundamental feature of system designs and their associated design flows and tools need to change, so that, to support the scalable parallelism and the design portability. The same feature can be exploited to design reconfigurable hardware, such as FPGAs, which leads to rethink the mapping of sequential algorithms to HDL. The sequential programming paradigm, widely used for programming single processor systems, does not naturally provide explicit or implicit forms of scalable parallelism. Conversely, dataflow programming is an approach that naturally provides parallelism and the potential to unify SW and HDL designs on heterogeneous platforms. This study describes a dataflow-based design methodology aiming at a unified co-design and co-synthesis of heterogeneous systems. Experimental results on the implementation of a JPEG codec and a MPEG 4 SP decoder on heterogeneous platforms demonstrate the flexibility and capabilities of this design approach

    Hardware synthesis of complex standard interfaces using CAL dataflow descriptions

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    This paper presents a contribution to the development of rapid prototyping tools based on data-flow description. In this context, the efficiency of automatic translator tools from the data-flow description to C and/or HDL are presented using two design cases. Moreover, this paper presents the novel concept of the automatic synthesis of interfaces based on dataflow description. Such “generic” interfaces include an embedded microprocessor, which enables using a vide variety of interfaces already available as optimized libraries from the FPGA manufacturers. The different design cases described have been tested and validated on different platforms. The results of the work show the flexibility and generality of the proposed wrapper methodology that is described in the paper

    Design space exploration strategies for FPGA implementation of signal processing systems using CAL dataflow program

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    This paper presents some strategies for design space exploration of FPGA-based signal processing systems that are specified using the CAL dataflow language. The actor- oriented, high-level of abstraction provided by CAL allows flexible exploration and consequently results in a wide range of feasible design implementations. We have applied and ex- tended the existing techniques for refactoring and pipelining actors and actions by means of critical path analysis, and in- troduced some new buffering techniques based on heuristics. The combinations of these techniques have been applied on the CAL specification of the MPEG-4 video decoder, and synthesized to HDL for evaluation in the design implementa- tion space. Results show that using our configuration for the exploration of 48 design points, a throughput range of roughly 8x has been achieved, for slice, block RAM, frequency, and latency range of 1.3x, 2.5x, 2.5x, and 2.9x respectively

    High-level dataflow design of signal processing systems for reconfigurable and multicore heterogeneous platforms

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    The potential computational power of today multicore processors has drastically improved compared to the single processor architecture. Since the trend of increasing the processor frequency is almost over, the competition for increased performance has moved on the number of cores. Consequently, the fundamental feature of system designs and their associated design flows and tools need to change, so that, to support the scalable parallelism and the design portability. The same feature can be exploited to design reconfigurable hardware, such as FPGAs, which leads to rethink the mapping of sequential algorithms to HDL. The sequential programming paradigm, widely used for programming single processor systems, does not naturally provide explicit or implicit forms of scalable parallelism. Conversely, dataflow programming is an approach that naturally provides parallelism and the potential to unify SW and HDL designs on heterogeneous platforms. This study describes a dataflow-based design methodology aiming at a unified co-design and co-synthesis of heterogeneous systems. Experimental results on the implementation of a JPEG codec and a MPEG 4 SP decoder on heterogeneous platforms demonstrate the flexibility and capabilities of this design approach

    Generation of Hardware/Software systems based on CAL dataflow description

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    International audienceThis paper presents a new development of rapid prototyping tools for system design based on data-flow specifications. In this context, the efficiency of tools for the automatic translation from the data-flow programs to C and/or HDL are assessed by means of two design cases. The paper also introduces the new concept of the automatic synthesis of interfaces. Such generic interfaces are implemented by using an embedded microprocessor, which can support a large variety of interfaces already available as native IP libraries in the case of FPGA. The two design cases described here have been developed, tested and validated on different implementation platforms. The results of the assessment show that flexibility, genericity and generality are attractive features of the proposed interface implementation methodology approach

    Secure Computing with the MPEG RVC Framework

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    Recently, ISO/IEC standardized a dataflow-programming framework called Reconfigurable Video Coding (RVC) for the specification of video codecs. The RVC framework aims at providing the specification of a system at a high abstraction level so that the functionality (or behavior) of the system become independent of implementation details. The idea is to specify a system so that only intrinsic features of the algorithms are explicitly expressed, whereas implementation choices can then be made only once specific target platforms have been chosen. With this system design approach, one abstract design can be used to automatically create implementations towards multiple target platforms. In this paper, we report our investigations on applying the methodology standardized by the MPEG RVC framework to develop secure computing in the domains of cryptography and multimedia security, leading to the conclusion that the RVC framework can successfully be applied as a general-purpose framework to other fields beyond multimedia coding. This paper also highlights the challenges we faced in conducting our study, and how our study helped the RVC and the secure computing communities benefited from each other. Our investigations started with the development of a Crypto Tools Library (CTL) based on RVC, which covers a number of widely used ciphers and cryptographic hash functions such as AES, Triple DES, ARC4 and SHA-2. Performance benchmarking results on the RVC-based AES and SHA-2 implementations in both C and Java revealed that the automatically generated implementations can achieve a comparable performance to some manually-written reference implementations. We also demonstrated that the RVC framework can easily produce implementations with multi-core support without any change to the RVC code. A security protocol for mutual authentication was also implemented to demonstrate how one can build heterogeneous systems easily with RVC. By combining CTL with Video Tool Library (a standard library defined by the RVC standard), a non-standard RVC-based H.264/AVC encoder and a non-standard RVC-based JPEG codec, we further demonstrated the benefits of using RVC to develop different kinds of multimedia security applications, which include joint multimedia encryption-compression schemes, digital watermarking and image steganography in JPEG compressed domain. Our study has shown that RVC can be used as a general-purpose implementation-independent development framework for diverse data-driven applications with different complexities
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