7,434 research outputs found
Statistical prediction with Kanerva's sparse distributed memory
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory
EffiTest: Efficient Delay Test and Statistical Prediction for Configuring Post-silicon Tunable Buffers
At nanometer manufacturing technology nodes, process variations significantly
affect circuit performance. To combat them, post- silicon clock tuning buffers
can be deployed to balance timing bud- gets of critical paths for each
individual chip after manufacturing. The challenge of this method is that path
delays should be mea- sured for each chip to configure the tuning buffers
properly. Current methods for this delay measurement rely on path-wise
frequency stepping. This strategy, however, requires too much time from ex-
pensive testers. In this paper, we propose an efficient delay test framework
(EffiTest) to solve the post-silicon testing problem by aligning path delays
using the already-existing tuning buffers in the circuit. In addition, we only
test representative paths and the delays of other paths are estimated by
statistical delay prediction. Exper- imental results demonstrate that the
proposed method can reduce the number of frequency stepping iterations by more
than 94% with only a slight yield loss.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
Statistical Prediction of Peaks Over a Threshold
In many applied fields it is desired to make predictions with the aim of
assessing the plausibility of more severe events than those already recorded to
safeguard against calamities that have not yet occurred. This problem can be
analysed using extreme value theory. We consider the popular peaks over a
threshold method and show that the generalised Pareto approximation of the true
predictive densities of both a future unobservable excess or peak random
variable can be very accurate. We propose both a frequentist and a Bayesian
approach for the estimation of such predictive densities. We show the
asymptotic accuracy of the corresponding estimators and, more importantly,
prove that the resulting predictive inference is asymptotically reliable. We
show the utility of the proposed predictive tools analysing extreme
temperatures in Milan in Italy
Statistical prediction of seasonal air temperature over Eurasia
Statistical models for the prediction of seasonal surface air temperature anomalies over Eurasia were constructed. The models were designed to test the relative predictive skill of Atlantic sea surface temperatures (SST), sea level pressure (SLP) and persistence in a cyclostationary setting. Significant forecast skill was found for the spring season in central and eastern Europe. The main predictors were persistence and SLPs (the north Atlantic oscillation). SSTs had little predictive value. All results were confirmed with independent forecast experiments. The statistical results were attributed to (a) a positive feedback between given winter atmospheric circulation regimes, the snow cover they produce and the snow-induced enhancement/retardation of normal season warming and (b) the persistence of large-scale circulation patterns over the Atlantic Ocean
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