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Memory-level parallelism

Known as: MLP, Memory Level Parallelism 
Memory-level parallelism (MLP) is a term in computer architecture referring to the ability to have pending multiple memory operations, in particular… Expand
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Papers overview

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Highly Cited
2017
Highly Cited
2017
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This… Expand
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Highly Cited
2016
Highly Cited
2016
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time… Expand
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Highly Cited
2011
Highly Cited
2011
The multi-level perspective (MLP) has emerged as a fruitful middle-range framework for analysing socio-technical transitions to… Expand
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Highly Cited
2011
Highly Cited
2011
We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty… Expand
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Highly Cited
2010
Highly Cited
2010
We present automatic data layout transformation as an effective compiler performance optimization for memory-bound structured… Expand
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Highly Cited
2009
Highly Cited
2009
DRAM systems achieve high performance when all DRAM banks are busy servicing useful memory requests. The degree to which DRAM… Expand
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Highly Cited
2004
Highly Cited
2004
The performance of memory-bound commercial applications such as databases is limited by increasing memory latencies. In this… Expand
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Highly Cited
1998
Highly Cited
1998
Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these… Expand
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Highly Cited
1995
Highly Cited
1995
A wavelet-based neural network is described. The structure of this network is similar to that of the radial basis function (RBF… Expand
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Highly Cited
1990
Highly Cited
1990
The statistical use of a particular classic form of a connectionist system, the multilayer perceptron (MLP), is described in the… Expand
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