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One of the pitfalls of FPGA design is the relatively long implementation time when compared to alternative architectures, such as CPU, GPU or DSP. This time can be greatly reduced however by using tools that can generate hardware systems in the form of a hardware description language (HDL) from high-level languages such as C, C++, or Python. Such(More)
—One of the main challenges of using heterogeneous systems results from the need to find the computation-to-hardware assignments that maximize the overall application performance. The important computational factors that must be taken into account include algorithmic complexity, exploitable parallelism, memory bandwidth requirements, and data size. To(More)
One of the main challenges of using cutting edge medical imaging applications in the clinical setting is the large amount of data processing required. Many of these applications are based on linear algebra computations operating on large data sizes and their execution may require days in a standard CPU. Distributed heterogeneous systems are capable of(More)
—As tools for designing multiple processor systems-on-chips (MPSoCs) continue to evolve to meet the demands of developers, there exist systematic gaps that must be bridged to provide a more cohesive hardware/software development environment. We present Redsharc to address these problems and enable: system generation, software/hardware compilation and(More)
In this paper we propose a new degree of flexibility for soft processor design in which only the instructions relevant to the task at hand are implemented as a subset of the Instruction Set Architecture (ISA). These customized processors execute software kernels in the usual way, yet can be implemented with a fraction of the hardware resources used by other(More)
FPGAs have been shown to provide orders of magnitude improvement over CPUs and GPUs in terms of absolute performance and energy efficiency for various kernels such as Cholesky decomposition, matrix inversion, and FFT among others. Despite this, the overall performance of many applications suffer when implemented entirely in FPGAs. Combining FPGAs with CPUs(More)
Compute-intensive applications incorporate ever increasing data processing requirements on hardware systems. Many of these applications have only recently become feasible thanks to the increasing computing power of modern processors. The Matlab language is uniquely situated to support the description of these compute-intensive scientific applications, and(More)
With the continual enhancement of heterogeneous resources in FPGA devices, utilizing these resources becomes a challenging burden for developers. Especially with the inclusion of sophisticated multiple processor system-on-chips, the necessary skill set to effectively leverage these resources spans both hardware and software expertise. The maturation of high(More)