Emmanuel Amaro

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A growing number of commercial and enterprise systems increasingly rely on compute-intensive machine learning algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FP-GAs) provide a promising path forward to accommodate the(More)
Approximate computing trades quality of application output for higher efficiency and performance. Approximation is useful only if its impact on application output quality is acceptable to the users. However, there is a lack of systematic solutions and studies that explore users' perspective on the effects of approximation. In this paper, we seek to provide(More)
Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they offer a programmable substrate for acceleration and are becoming available across different market segments. However, obtaining both performance and energy efficiency with FPGAs is a(More)
—Cyclic instabilities can impact the performance of a multi agent system, especially in terms of the user's point of view. Different strategies can be use in order to prevent this problem. In this paper we present two strategies, ONL1 and ONL2 that aim at minimizing the collateral consequences of locking. These two strategies focus on minimizing the number(More)
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