Ericles Rodrigues Sousa

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Nowadays, computer vision algorithms have countless application domains. On the one hand, these algorithms are typically computationally demanding, on the other hand, they are often used in embedded systems, which have stringent constraints on, e. g., size or power. In this work, we present the benefits of mapping compute-intensive imaging algorithms on(More)
Continuous software and hardware innovations impose on the one hand a high degree of flexibility from an algorithm and on the other hand it requires that a given processing architecture has the capability to adapt to changing computation patterns at run-time. In this work, we demonstrate how a computer vision application can adapt itself at runtime in order(More)
Recently, DSP and FPGA devices have been employed in cooperative computing architectures for embedded systems which has required high complexity computing process which in turn demanded efficient techniques capable of measuring the total effective gain of the partitioning of a given code. In order to meet that need and based on Amdahl's Law a mathematical(More)
This paper describes a runtime reconfigurable bus arbitration technique for concurrent applications on heterogeneous MPSoC architectures. Here, a hardware/software approach is introduced as part of a runtime framework that enables selecting and adapting different policies (i. e., fixed-priority, TDMA, and Round-Robin) such that the performance goals of(More)
Massively Parallel Processor Arrays (MPPAs) can be nicely used in portable devices such as tablets and smartphones. However, applications running on mobile platforms require a certain performance level or quality (e.g., high-resolution image processing) that need to be satisfied while adhering to a certain power budget and temperature threshold. As a(More)
The recent years have shown the emergence of heterogeneous system architecture (HSA), which offers massive computational power assembled into a compact design. Computer vision applications with massive inherent parallelism highly benefits from such heterogeneous processors with on-chip CPU and GPU units. The highly parallel and compute intensive parts of(More)