Toward efficient large-scale performance modeling of integrated circuits via multi-mode/multi-corner sparse regression

Authors:
Wangyang Zhang;Tsung-Hao Chen;Ming-Yuan Ting;Xin Li
Affiliations:
Mentor Graphics Corporation, San Jose, CA;Mentor Graphics Corporation, San Jose, CA;Mentor Graphics Corporation, San Jose, CA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 47th Design Automation Conference
Year:
2010

Citing 10
Cited 1

Mismatch analysis and direct yield optimization by specwise linearization and feasibility-guided search

Proceedings of the 38th annual Design Automation Conference
Timing

Timing
Projection-based performance modeling for inter/intra-die variations

ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Performance-oriented statistical parameter reduction of parameterized systems via reduced rank regression

Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Beyond low-order statistical response surfaces: latent variable regression for efficient, highly nonlinear fitting

Proceedings of the 44th annual Design Automation Conference
Principle Hessian direction based parameter reduction with process variation

Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Statistical regression for efficient high-dimensional modeling of analog and mixed-signal performance variations

Proceedings of the 45th annual Design Automation Conference
Finding deterministic solution from underdetermined equation: large-scale performance modeling by least angle regression

Proceedings of the 46th Annual Design Automation Conference
Multitask compressive sensing

IEEE Transactions on Signal Processing
Template-free symbolic performance modeling of analog circuits via canonical-form functions and genetic programming

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation

Proceedings of the 50th Annual Design Automation Conference

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a novel multi-mode/multi-corner sparse regression (MSR) algorithm to build large-scale performance models of integrated circuits at multiple working modes and environmental corners. Our goal is to efficiently extract multiple performance models to cover different modes/corners with a small number of simulation samples. To this end, an efficient Bayesian inference with shared prior distribution (i.e., model template) is developed to explore the strong performance correlation among different modes/corners in order to achieve high modeling accuracy with low computational cost. Several industrial circuit examples demonstrate that the proposed MSR achieves up to 185x speedup over least-squares regression [14] and 6.7x speedup over least-angle regression [7] without surrendering any accuracy.