Preethi Venkatesan

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This paper focuses on the trade-off between flexibility and efficiency in specialized computing. We observe that specialized units achieve most of their efficiency gains by tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the kernels. Hence, by identifying key data-flow patterns used in a(More)
General-purpose processors, while tremendously versatile, pay a huge cost for their flexibility by wasting over 99% of the energy in programmability overheads. We observe that reducing this waste requires tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the algorithms. Hence, by backing off(More)
In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of(More)
This paper proposes a pipelined, systolic architecture for two-dimensional discrete Fourier transform (DFT) computation which is highly concurrent. The architecture consists of two, one-dimensional DFT blocks connected via an intermediate buffer. The proposed architecture offers low latency as well as high throughput and can perform both one-and(More)
Image compression is to reduce irrelevance data and redundancy of the image data in order to be able to store or transmit data in an efficient form. Image compression scheme either be in lossy method or lossless method. Lossy algorithms are especially suitable for transmit images across the network with minor (sometimes imperceptible) loss of fidelity of(More)
This paper presents a survey of Hybrid fuzzy c-means (FCM) clustering algorithms, The algorithmic steps, parameters involved in the algorithm & the experimental results on various datasets of several hybrid clustering methods are discussed in this paper. Hybrid FCM clustering techniques are obtained by modifying the FCM either by incorporating(More)
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