The Experts below are selected from a list of 198 Experts worldwide ranked by ideXlab platform
Janet Roveda - One of the best experts on this subject based on the ideXlab platform.
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a new uncertainty budgeting based method for Robust Analog mixed signal design
ACM Transactions on Design Automation of Electronic Systems, 2015Co-Authors: Jin Sun, Priyank Gupta, Claudio Talarico, Janet RovedaAbstract:This article proposes a novel methodology for Robust Analog/mixed-signal IC design by introducing a notion of budget of uncertainty. This method employs a new conic uncertainty model to capture process variability and describes variability-affected circuit design as a set-based Robust optimization problem. For a prespecified yield requirement, the proposed method conducts uncertainty budgeting by associating performance yield with the size of uncertainty set for process variations. Hence the uncertainty budgeting problem can be further translated into a tractable Robust optimization problem. Compared with the existing Robust design flow based on ellipsoid model, this method is able to produce more reliable design solutions by allowing varying size of conic uncertainty set at different design points. In addition, the proposed method addresses the limitation that the size of the ellipsoid model is calculated solely relying on the distribution of process parameters, while neglecting the dependence of circuit performance upon these design parameters. The proposed Robust design framework has been verified on various Analog/mixed-signal circuits to demonstrate its efficiency against the ellipsoid model. Up to 24p reduction of design cost has been achieved by using the uncertainty budgeting-based method.
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a new uncertainty budgeting based method for Robust Analog mixed signal design
Design Automation Conference, 2012Co-Authors: Jin Sun, Priyank Gupta, Janet RovedaAbstract:This paper proposes a novel methodology for Robust Analog/mixed-signal IC design by introducing a notion of budget of uncertainty. This method employs a new conic uncertainty model to capture process variability and describes variability-affected circuit design as a set-based Robust optimization problem. For a pre-specified yield requirement, the proposed method conducts uncertainty budgeting by associating performance yield with the size of uncertainty set for process variations. Hence the uncertainty budgeting problem can be further translated into a tractable Robust optimization problem. Compared with the existing Robust design flow based on ellipsoid model, this method is able to produce more reliable design solutions by allowing varying size of conic uncertainty set at different design points. In addition, the proposed method addresses the limitation that the size of ellipsoid model is calculated solely relying on the distribution of process parameters, while neglecting the dependence of circuit performance upon these design parameters. The proposed Robust design framework has been verified on various Analog/mixed-signal circuits to demonstrate its efficiency against ellipsoid model. An up to 24% reduction of design cost has been achieved by using the uncertainty budgeting based method.
Larry Pileggi - One of the best experts on this subject based on the ideXlab platform.
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Robust Analog rf circuit design with projection based performance modeling
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2007Co-Authors: Xin Li, P Gopalakrishnan, Yang Xu, Larry PileggiAbstract:In this paper, a Robust Analog design (ROAD) tool for post-tuning (i.e., locally optimizing) Analog/RF circuits is proposed. Starting from an initial design derived from hand analysis or Analog circuit optimization based on simplified models, ROAD extracts accurate performance models via transistor-level simulation and iteratively improves the circuit performance by a sequence of geometric programming steps. Importantly, ROAD sets up all design constraints to include large-scale process and environmental variations, thereby facilitating the tradeoff between yield and performance. A crucial component of ROAD is a novel projection-based scheme for quadratic (both polynomial and posynomial) performance modeling, which allows our approach to scale well to large problem sizes. A key feature of this projection-based scheme is a new implicit power iteration algorithm to find the optimal projection space and extract the unknown model coefficients with Robust convergence. The efficacy of ROAD is demonstrated on several circuit examples
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opera optimization with ellipsoidal uncertainty for Robust Analog ic design
Design Automation Conference, 2005Co-Authors: Kanlin Hsiung, I Nausieda, Stephen Boyd, Larry PileggiAbstract:As the design-manufacturing interface becomes increasingly complicated with IC technology scaling, the corresponding process variability poses great challenges for nanoscale Analog/RF design. Design optimization based on the enumeration of process corners has been widely used , but can suffer from inefficiency and overdesign. In this paper we propose to formulate the Analog and RF design with variability problem as a special type of Robust optimization problem, namely Robust geometric programming. The statistical variations in both the process parameters and design variables are captured by a pre-specified confidence ellipsoid. Using such optimization with ellipsoidal uncertainy approach, Robust design can be obtained with guaranteed yield bound and lower design cost, and most importantly, the problem size grows linearly with number of uncertain parameters. Numerical examples demonstrate the efficiency and reveal the trade-off between the design cost versus the yield requirement. We will also demonstrate significant improvement in the design cost using this approach compared with corner-enumeration optimization.
Slawomir Stanczak - One of the best experts on this subject based on the ideXlab platform.
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Robust Analog function computation via wireless multiple access channels
IEEE Transactions on Communications, 2013Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Wireless sensor network applications often involve the computation of pre-defined functions of the measurements such as for example the arithmetic mean or maximum value. Standard approaches to this problem separate communication from computation: digitized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings at the fusion center is not necessary to compute a function of them. In particular, if the mathematical structure of the channel is suitably matched (in some sense) to the function of interest, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. This paper proposes an Analog computation scheme that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. A match between the channel and the function being evaluated is thereby achieved via some pre-processing on the sensor readings and post-processing on the superimposed signals observed by the fusion center. After analyzing the estimation error for two function examples, simulations are presented to show the potential for huge performance gains over time- and code-division multiple-access based computation schemes.
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Robust Analog function computation via wireless multiple access channels
arXiv: Information Theory, 2012Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Various wireless sensor network applications involve the computation of a pre-defined function of the measurements without the need for reconstructing each individual sensor reading. Widely-considered examples of such functions include the arithmetic mean and the maximum value. Standard approaches to the computation problem separate computation from communication: quantized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings is not necessary for the fusion center to compute a function of them. In particular, if the mathematical structure of the wireless channel is suitably matched (in some sense) to the function, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. Therefore, in this paper a practically relevant Analog computation scheme is proposed that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. After analyzing the asymptotic properties of the estimation error, numerical simulations are presented to show the potential for huge performance gains when compared with time-division multiple-access based computation schemes.
Jin Sun - One of the best experts on this subject based on the ideXlab platform.
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a new uncertainty budgeting based method for Robust Analog mixed signal design
ACM Transactions on Design Automation of Electronic Systems, 2015Co-Authors: Jin Sun, Priyank Gupta, Claudio Talarico, Janet RovedaAbstract:This article proposes a novel methodology for Robust Analog/mixed-signal IC design by introducing a notion of budget of uncertainty. This method employs a new conic uncertainty model to capture process variability and describes variability-affected circuit design as a set-based Robust optimization problem. For a prespecified yield requirement, the proposed method conducts uncertainty budgeting by associating performance yield with the size of uncertainty set for process variations. Hence the uncertainty budgeting problem can be further translated into a tractable Robust optimization problem. Compared with the existing Robust design flow based on ellipsoid model, this method is able to produce more reliable design solutions by allowing varying size of conic uncertainty set at different design points. In addition, the proposed method addresses the limitation that the size of the ellipsoid model is calculated solely relying on the distribution of process parameters, while neglecting the dependence of circuit performance upon these design parameters. The proposed Robust design framework has been verified on various Analog/mixed-signal circuits to demonstrate its efficiency against the ellipsoid model. Up to 24p reduction of design cost has been achieved by using the uncertainty budgeting-based method.
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a new uncertainty budgeting based method for Robust Analog mixed signal design
Design Automation Conference, 2012Co-Authors: Jin Sun, Priyank Gupta, Janet RovedaAbstract:This paper proposes a novel methodology for Robust Analog/mixed-signal IC design by introducing a notion of budget of uncertainty. This method employs a new conic uncertainty model to capture process variability and describes variability-affected circuit design as a set-based Robust optimization problem. For a pre-specified yield requirement, the proposed method conducts uncertainty budgeting by associating performance yield with the size of uncertainty set for process variations. Hence the uncertainty budgeting problem can be further translated into a tractable Robust optimization problem. Compared with the existing Robust design flow based on ellipsoid model, this method is able to produce more reliable design solutions by allowing varying size of conic uncertainty set at different design points. In addition, the proposed method addresses the limitation that the size of ellipsoid model is calculated solely relying on the distribution of process parameters, while neglecting the dependence of circuit performance upon these design parameters. The proposed Robust design framework has been verified on various Analog/mixed-signal circuits to demonstrate its efficiency against ellipsoid model. An up to 24% reduction of design cost has been achieved by using the uncertainty budgeting based method.
Mario Goldenbaum - One of the best experts on this subject based on the ideXlab platform.
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Robust Analog function computation via wireless multiple access channels
IEEE Transactions on Communications, 2013Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Wireless sensor network applications often involve the computation of pre-defined functions of the measurements such as for example the arithmetic mean or maximum value. Standard approaches to this problem separate communication from computation: digitized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings at the fusion center is not necessary to compute a function of them. In particular, if the mathematical structure of the channel is suitably matched (in some sense) to the function of interest, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. This paper proposes an Analog computation scheme that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. A match between the channel and the function being evaluated is thereby achieved via some pre-processing on the sensor readings and post-processing on the superimposed signals observed by the fusion center. After analyzing the estimation error for two function examples, simulations are presented to show the potential for huge performance gains over time- and code-division multiple-access based computation schemes.
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Robust Analog function computation via wireless multiple access channels
arXiv: Information Theory, 2012Co-Authors: Mario Goldenbaum, Slawomir StanczakAbstract:Various wireless sensor network applications involve the computation of a pre-defined function of the measurements without the need for reconstructing each individual sensor reading. Widely-considered examples of such functions include the arithmetic mean and the maximum value. Standard approaches to the computation problem separate computation from communication: quantized sensor readings are transmitted interference-free to a fusion center that reconstructs each sensor reading and subsequently computes the sought function value. Such separation-based computation schemes are generally highly inefficient as a complete reconstruction of individual sensor readings is not necessary for the fusion center to compute a function of them. In particular, if the mathematical structure of the wireless channel is suitably matched (in some sense) to the function, then channel collisions induced by concurrent transmissions of different nodes can be beneficially exploited for computation purposes. Therefore, in this paper a practically relevant Analog computation scheme is proposed that allows for an efficient estimate of linear and nonlinear functions over the wireless multiple-access channel. After analyzing the asymptotic properties of the estimation error, numerical simulations are presented to show the potential for huge performance gains when compared with time-division multiple-access based computation schemes.
