Automatic compilation method and framework for generating a layout of integrated memory-compute circuit
US-2024403527-A1 · Dec 5, 2024 · US
US2016306903A9 · US · A9
| Field | Value |
|---|---|
| Publication number | US-2016306903-A9 |
| Application number | US-201314047879-A |
| Country | US |
| Kind code | A9 |
| Filing date | Oct 7, 2013 |
| Priority date | Apr 14, 2011 |
| Publication date | Oct 20, 2016 |
| Grant date | — |
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Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.
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1 . A method of predicting a metric of a physical system using a semiparametric model, comprising: providing raw data representative of the physical system; processing the raw data to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred; estimating a nonparametric component of the semiparametric model with reference to the set of units, the set of times, and the index-set; and predicting a hazard rate at a given time with the semiparametric model. 2 . The method of claim 1 , further comprising estimating a parametric component of the semiparametric model with reference to the set of units, the set of times, and the index-set. 3 . The method of claim 1 , wherein the event is a failure event. 4 . The method of claim 1 , wherein the metric is a failure metric. 5 . The method of claim 1 , wherein the metric comprises a mean time between failures. 6 . The method of claim 1 , further comprising storing the set of units, the set of times of treatment, and the index-set. 7 . The method of claim 1 , wherein providing raw data further comprises providing raw data in real time. 8 . The method of claim 1 , wherein the physical system is a cyber-physical system. 9 . The method of claim 1 , wherein the physical system is an electrical grid. 10 . The method of claim 1 , wherein the raw data represents an outage database. 11 . The method of claim 1 , wherein each treatment in the set of times of treatment comprises a single “all-or-nothing” treatment occurring at a recorded time. 12 . The method of claim 2 , further comprising: estimating the nonparametric component as zero for all times except those included in the set of times of treatment and estimating the parametric component; and estimating the nonparametric component using a weighted nonparametric estimator using a the estimate of the parametric component. 13 . The method of claim 1 , further comprising: removing from the index-set units for which the times at which a event occurs is unknown or for which the treatment is unknown. 14 . The method of claim 1 , further comprising smoothing the nonparametric component with a smoothing process. 15 . The method of claim 14 , wherein the smoothing process is a Gaussian smoothing process. 16 . The method of claim 2 , wherein the nonparametric component is given by λ 0 (t), the parametric component is given by ψ(t)= φ(t) , the hazard rate is predicted with reference to the semiparametric model given by λ(t;i)=λ 0 (t)ψ(t−τ l,i ), and a full likelihood of failure is given by l ( λ 0 ( · ) , ψ ( · ) ) = ( ∏ t ∈ t λ 0 ( t ) ψ ( t - τ i , t ( t ) ) ) × - ∫ 0 T ∑ j ∈ ( t ) λ 0 ( t ) ψ ( t - τ t , i ( t )
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