Katayoun Neshatpour

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A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization. Considering the time dedicated to create such hardware implementations, an analysis that estimates how much benefit we gain in terms of speed and energy efficiency, through offloading various functions to hardware would be necessary.(More)
Big data applications share inherent characteristics that are fundamentally different from traditional desktop CPU, parallel and web service applications. They rely on deep machine learning and data mining applications. A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization to construct(More)
  • Friday, Blockinalex Blockinjones, +131 authors Qiaosha Zou
  • 2014
4 Welcome Welcome to the 24th edition of the Great Lakes Symposium on VLSI (GLSVLSI) 2014 held in Houston, Texas. GLSVLSI is a premier venue for the dissemination of manuscripts of the highest quality BLOCKINin BLOCKINall BLOCKINareas BLOCKINrelated BLOCKINto BLOCKINVLSI, BLOCKINdevices BLOCKINand BLOCKINsystem BLOCKINlevel BLOCKINdesign. BLOCKINThe(More)
Emerging big data analytics applications require a significant amount of server computational power. As chips are hitting power limits, computing systems are moving away from general-purpose designs and toward greater specialization. Hardware acceleration through specialization has received renewed interest in recent years, mainly due to the dark silicon(More)
As CMOS technology scales down towards nanometer regime and the supply voltage approaches the threshold voltage, increase in operating temperature results in increased circuit current, which in turn reduces circuit propagation delay. This paper exploits this new phenomenon, known as inverse thermal dependence (ITD) for power, performance, and temperature(More)
In this paper, we present the implementation of big data analytics applications in a heterogeneous CPU+FPGA accelerator architecture. We develop the MapReduce implementation of K-means, K nearest neighbor, support vector machine and Naive Bayes in a Hadoop Streaming environment that allows developing mapper/reducer functions in a non-Java based language(More)
The rapid growth in the data yields challenges to process data efficiently using current high-performance server architectures such as big Xeon cores. Furthermore, physical design constraints, such as power and density, have become the dominant limiting factor for scaling out servers. Heterogeneous architectures that combine big Xeon cores with little Atom(More)
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