A big-data approach to handle process variations: Uncertainty quantification by tensor recovery

Zhang, Zheng; Weng, Tsui-Wei; Daniel, Luca

doi:10.1109/sapiw.2016.7496314

Cited by 12 publications

(8 citation statements)

References 39 publications

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“…In side channel analysis [7]- [13], the impacts of HTs (e.g., circuit delay, transient current, leakage power and heat analysis) are used to detect whether there are the HTs in CUD. However, the characteristics of circuit is more susceptible to process variations and environmental noise due to the present nanoscale technologies [14].…”

Section: Related Workmentioning

confidence: 99%

HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

Lu¹,

Shen²,

Zhang³

et al. 2019

Preprint

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Hardware Trojans (HTs) have drawn more and more attention in both academia and industry because of its significant potential threat. In this paper, we proposed a novel HT detection method using information entropy based clustering, named HTDet. The key insight of HTDet is that the Trojan usually be inserted in the regions with low controllability and low observability in order to maintain high concealment, which will result in that Trojan logics appear extremely low transitions during the simulation. This means that the logical regions with the low transitions will provide us with much more abundant and more important information for HT detection. Therefore, HTDet applies information theory technology and a typical density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in circuit under detection (CUD). DB-SCAN is an unsupervised learning algorithm, which can improve the applicability of HTDet. Besides, we develop a heuristic test patterns generation method using mutual information to increase the transitions of suspicious Trojan logics. Experimental evaluation with benchmarks demenstrates the effectiveness of HTDet.

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Section: Related Workmentioning

confidence: 99%

HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

Lu¹,

Shen²,

Zhang³

et al. 2019

Preprint

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“…, z m ] with z k = A, W k , one may find that many elements of z are close to zero. To exploit the low-rank and sparse properties simultaneously, the following optimization problem [98], [99] may be solved:…”

Section: B Regularized Tensor Completionmentioning

confidence: 99%

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Zhang

Batselier

Liu

et al. 2017

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. fullchip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently highdimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.

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“…Low-rank tensor approximation has shown promising performance in solving high-dimensional uncertainty quantification problems [24][25][26][27][28][29]. By low-rank tensor decomposition, one may reduce the number of unknown variables in uncertainty quantification to a linear function of the parameter dimensionality.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Uncertainty Quantification via Active and Rank-Adaptive Tensor Regression

Zhang

2020

2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

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Fabrication process variations can significantly influence the performance and yield of nano-scale electronic and photonic circuits. Stochastic spectral methods have achieved great success in quantifying the impact of process variations, but they suffer from the curse of dimensionality. Recently, low-rank tensor methods have been developed to mitigate this issue, but two fundamental challenges remain open: how to automatically determine the tensor rank and how to adaptively pick the informative simulation samples. This paper proposes a novel tensor regression method to address these two challenges. We use a q / 2 group-sparsity regularization to determine the tensor rank. The resulting optimization problem can be efficiently solved via an alternating minimization solver. We also propose a twostage adaptive sampling method to reduce the simulation cost. Our method considers both exploration and exploitation via the estimated Voronoi cell volume and nonlinearity measurement respectively. The proposed model is verified with synthetic and some realistic circuit benchmarks, on which our method can well capture the uncertainty caused by 19 to 100 random variables with only 100 to 600 simulation samples.

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A big-data approach to handle process variations: Uncertainty quantification by tensor recovery

Cited by 12 publications

References 39 publications

HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

Tensor Computation: A New Framework for High-Dimensional Problems in EDA

High-Dimensional Uncertainty Quantification via Active and Rank-Adaptive Tensor Regression

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