7,644 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
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
We study online prediction where regret of the algorithm is measured against
a benchmark defined via evolving constraints. This framework captures online
prediction on graphs, as well as other prediction problems with combinatorial
structure. A key aspect here is that finding the optimal benchmark predictor
(even in hindsight, given all the data) might be computationally hard due to
the combinatorial nature of the constraints. Despite this, we provide
polynomial-time \emph{prediction} algorithms that achieve low regret against
combinatorial benchmark sets. We do so by building improper learning algorithms
based on two ideas that work together. The first is to alleviate part of the
computational burden through random playout, and the second is to employ
Lasserre semidefinite hierarchies to approximate the resulting integer program.
Interestingly, for our prediction algorithms, we only need to compute the
values of the semidefinite programs and not the rounded solutions. However, the
integrality gap for Lasserre hierarchy \emph{does} enter the generic regret
bound in terms of Rademacher complexity of the benchmark set. This establishes
a trade-off between the computation time and the regret bound of the algorithm
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