973 research outputs found
Evaluation Mappings of Spatial Accelerator Based On Data Placement
The scheduling strategies of workloads are critical to fully exploiting the
performance of spatial accelerators, accurate performance models are required
to evaluate the mapping of workloads.Recent works proposed various cost-model
to describe the dataflow of the spatial accelerator. However, they are less
expressive about customized memory hierarchies and thus lead to inaccurate
performance models. In this paper, we propose, PolyAcc, a framework for
evaluating the mappings of workload on spatial accelerator based on data
placement. The Data placement relation describes the temporal-spatial relation
of data at different memory levels, which can accurately capture the runtime
behavior of hardware units. Based on data placement relations, polyAcc
accurately analyzes the data volume for different reuse patterns and estimate
metrics, including data reuse, latency, and energy. Overall, polyAcc closely
matches the ideal execution time and PE utilization for GEMM and Conv
workloads, respectively achieves 0.82%, 18.8% improvements for execution time
and energy consumption estimates in validation against Eyeriss architecture
compared to the state-of-the-art framework.Comment: 7 pages,8 figures,3 table
Robust High-dimensional Tuning Free Multiple Testing
A stylized feature of high-dimensional data is that many variables have heavy
tails, and robust statistical inference is critical for valid large-scale
statistical inference. Yet, the existing developments such as Winsorization,
Huberization and median of means require the bounded second moments and involve
variable-dependent tuning parameters, which hamper their fidelity in
applications to large-scale problems. To liberate these constraints, this paper
revisits the celebrated Hodges-Lehmann (HL) estimator for estimating location
parameters in both the one- and two-sample problems, from a non-asymptotic
perspective. Our study develops Berry-Esseen inequality and Cram\'{e}r type
moderate deviation for the HL estimator based on newly developed non-asymptotic
Bahadur representation, and builds data-driven confidence intervals via a
weighted bootstrap approach. These results allow us to extend the HL estimator
to large-scale studies and propose \emph{tuning-free} and \emph{moment-free}
high-dimensional inference procedures for testing global null and for
large-scale multiple testing with false discovery proportion control. It is
convincingly shown that the resulting tuning-free and moment-free methods
control false discovery proportion at a prescribed level. The simulation
studies lend further support to our developed theory.Comment: In this paper, we develop tuning-free and moment-free high
dimensional inference procedures
Spectral Ranking Inferences based on General Multiway Comparisons
This paper studies the performance of the spectral method in the estimation
and uncertainty quantification of the unobserved preference scores of compared
entities in a very general and more realistic setup in which the comparison
graph consists of hyper-edges of possible heterogeneous sizes and the number of
comparisons can be as low as one for a given hyper-edge. Such a setting is
pervasive in real applications, circumventing the need to specify the graph
randomness and the restrictive homogeneous sampling assumption imposed in the
commonly-used Bradley-Terry-Luce (BTL) or Plackett-Luce (PL) models.
Furthermore, in the scenarios when the BTL or PL models are appropriate, we
unravel the relationship between the spectral estimator and the Maximum
Likelihood Estimator (MLE). We discover that a two-step spectral method, where
we apply the optimal weighting estimated from the equal weighting vanilla
spectral method, can achieve the same asymptotic efficiency as the MLE. Given
the asymptotic distributions of the estimated preference scores, we also
introduce a comprehensive framework to carry out both one-sample and two-sample
ranking inferences, applicable to both fixed and random graph settings. It is
noteworthy that it is the first time effective two-sample rank testing methods
are proposed. Finally, we substantiate our findings via comprehensive numerical
simulations and subsequently apply our developed methodologies to perform
statistical inferences on statistics journals and movie rankings
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