Wavelet multiresolution complex network for analyzing multivariate nonlinear time series

Z. K. Gao was supported by National Natural Science Foundation of China under Grant No. 61473203, and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200Peer reviewedPostprin

Informed microarchitecture design space exploration using workload dynamics

Program runtime characteristics exhibit significant variation. As microprocessor architectures become more complex, their efficiency depends on the capability of adapting with workload dynamics. Moreover, with the approaching billion-transistor microprocessor era, it is not always economical or feasible to design processors with thermal cooling and reliability redundancy capabilities that target an application’s worst case scenario. Therefore, analyzing complex workload dynamics early, at the microarchitecture design stage, is crucial to forecast workload runtime behavior across architecture design alternatives and evaluate the efficiency of workload scenariobased architecture optimizations. Existing methods focus exclusively on predicting aggregated workload behavior. In this paper, we propose accurate and efficient techniques and models to reason about workload dynamics across the microarchitecture design space without using detailed cyclelevel simulations. Our proposed techniques employ waveletbased multiresolution decomposition and neural network based non-linear regression modeling. We extensively evaluate the efficiency of our predictive models in forecasting performance, power and reliability domain workload dynamics that the SPEC CPU 2000 benchmarks manifest on high-performance microprocessors with a microarchitecture design space that consists of 9 key parameters. Our results show that the models achieve high accuracy in revealing workload dynamic behavior across a large microarchitecture design space. We also demonstrate that the proposed techniques can be used to efficiently explore workload scenario-driven architecture optimizations. 1

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

A Multiscale Approach for Statistical Characterization of Functional Images

Increasingly, scientific studies yield functional image data, in which the observed data consist of sets of curves recorded on the pixels of the image. Examples include temporal brain response intensities measured by fMRI and NMR frequency spectra measured at each pixel. This article presents a new methodology for improving the characterization of pixels in functional imaging, formulated as a spatial curve clustering problem. Our method operates on curves as a unit. It is nonparametric and involves multiple stages: (i) wavelet thresholding, aggregation, and Neyman truncation to effectively reduce dimensionality; (ii) clustering based on an extended EM algorithm; and (iii) multiscale penalized dyadic partitioning to create a spatial segmentation. We motivate the different stages with theoretical considerations and arguments, and illustrate the overall procedure on simulated and real datasets. Our method appears to offer substantial improvements over monoscale pixel-wise methods. An Appendix which gives some theoretical justifications of the methodology, computer code, documentation and dataset are available in the online supplements