261 research outputs found
Power Optimizations in MTJ-based Neural Networks through Stochastic Computing
Artificial Neural Networks (ANNs) have found widespread applications in tasks
such as pattern recognition and image classification. However, hardware
implementations of ANNs using conventional binary arithmetic units are
computationally expensive, energy-intensive and have large area overheads.
Stochastic Computing (SC) is an emerging paradigm which replaces these
conventional units with simple logic circuits and is particularly suitable for
fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel
Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits.
In this work, we propose an energy-efficient use of MTJs, which exhibit
probabilistic switching behavior, as Stochastic Number Generators (SNGs), which
forms the basis of our NN implementation in the SC domain. Further, error
resilient target applications of NNs allow us to introduce Approximate
Computing, a framework wherein accuracy of computations is traded-off for
substantial reductions in power consumption. We propose approximating the
synaptic weights in our MTJ-based NN implementation, in ways brought about by
properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm
that can perform such approximations within a given error tolerance in a
single-layer NN in an optimal way owing to the convexity of the problem
formulation. We then use this algorithm and develop a heuristic approach for
approximating multi-layer NNs. To give a perspective of the effectiveness of
our approach, a 43% reduction in power consumption was obtained with less than
1% accuracy loss on a standard classification problem, with 26% being brought
about by the proposed algorithm.Comment: Accepted in the 2017 IEEE/ACM International Conference on Low Power
Electronics and Desig
Using percolated dependencies for phrase extraction in SMT
Statistical Machine Translation (SMT) systems rely heavily on the quality of the phrase pairs induced from large amounts of training data. Apart from the widely used method of heuristic learning of n-gram phrase translations from word alignments, there are numerous methods for extracting these phrase pairs. One such class of approaches uses translation information encoded in parallel treebanks to extract phrase pairs. Work to date has demonstrated the usefulness of translation models induced from both constituency structure trees and dependency structure trees. Both syntactic annotations rely on the existence of natural language parsers for both the source and target languages. We depart from the norm by directly obtaining dependency parses from constituency structures using head percolation tables. The paper investigates the use of aligned chunks induced from percolated dependencies in French–English SMT and contrasts it with the aforementioned extracted phrases.
We observe that adding phrase pairs from any other method improves translation performance over the baseline n-gram-based system, percolated dependencies are a good substitute for parsed dependencies, and that supplementing with our novel head percolation-induced chunks shows a general trend toward improving all system types across two data sets up to a 5.26% relative increase in BLEU
Predicting Multi-actor collaborations using Hypergraphs
Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}
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