ANACONDA: robust synthesis of analog circuits via stochastic pattern search

Phelps, R.; Krasnicki, M.; Rutenbar, Rob A.; Carley, L.R.; Hellums, J.R.

doi:10.1109/cicc.1999.777345

Cited by 26 publications

(14 citation statements)

References 20 publications

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“…Using the full nonlinear cost function based on the linearized objectives significantly reduces the total number of iterations in the optimization. The ANACONDA tool, on the other hand, uses a global optimization algorithm based on stochastic pattern search that inherently contains parallelism and, therefore, can easily be distributed over a pool of workstations, to try out and simulate 50 000 to 100 000 circuit candidates in a few hours [94]. MAELSTROM is the framework that provides the simulator encapsulation and the environment to distribute both the search tasks of the optimization algorithm as well as the circuit evaluations at every iteration of the optimization over parallel workstations in a network [95].…”

Section: Analog Circuit Synthesis and Optimizationmentioning

confidence: 99%

Computer-aided design of analog and mixed-signal integrated circuits

2000

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Section: Analog Circuit Synthesis and Optimizationmentioning

confidence: 99%

Computer-aided design of analog and mixed-signal integrated circuits

2000

Self Cite

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“…To keep all the transistors away from the subthreshold region with a margin of , the constraints to be satisfied are (11) On the other hand, to keep a transistor away from the linear region, we require for n-type or for p-type (12) where Note that in the S-H MOS model, is equal to . However, this new notation is introduced for ease of extending the design formulation for any other MOS model, such as the one described in [20].…”

Section: B Design Space Constraintsmentioning

confidence: 99%

“…To summarize the formulation which is given in this section, an op-amp design problem is a constrained optimization problem of the following form: minimize subject to (22) The objective function is given in (21) while various constraint functions are provided in (11) and (13)- (20). It is observed that, assuming constant , and ( , the constraint functions 's are posynomials of , , , and of various transistors.…”

Section: Performance Constraints and Objective Functionmentioning

confidence: 99%

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CMOS op-amp sizing using a geometric programming formulation

Mandal

Visvanathan

2001

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

150

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Abstract-The problem of CMOS op-amp circuit sizing is addressed here. Given a circuit and its performance specifications, the goal is to automatically determine the device sizes in order to meet the given performance specifications while minimizing a cost function, such as a weighted sum of the active area and power dissipation. The approach is based on the observation that the first order behavior of a MOS transistor in the saturation region is such that the cost and the constraint functions for this optimization problem can be modeled as posynomial in the design variables. The problem is then solved efficiently as a convex optimization problem. Second order effects are then handled by formulating the problem as one of solving a sequence of convex programs. Numerical experiments show that the solutions to the sequence of convex programs converge to the same design point for widely varying initial guesses. This strongly suggests that the approach is capable of determining the globally optimal solution to the problem. Accuracy of performance prediction in the sizing program (implemented in MATLAB) is maintained by using a newly proposed MOS transistor model and verified against detailed SPICE simulation.

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“…ANACONDA uses framework components from a companion synthesis tool, MAELSTROM [2]. ANACONDA has been successfully run on networks of 10-20 UNIX workstations, and currently runs Texas Instrument's proprietary TISpice circuit simulator as its evaluation engine [3]. In this paper, we extend the original treatment, describe in more detail the basic algorithms underlying ANACONDA, and present an expanded set of experimental synthesis results that demonstrate that simulator-in-the-loop synthesis can be made both practical and efficient for industrial designs.…”

mentioning

confidence: 99%

Anaconda: SimulationBased Synthesis of Analog Circuits Via Stochastic Pattern Search

Rutenbar¹,

Gielen²,

Antao³

2009

Computer-Aided Design of Analog Integrated Circuits and Systems

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Abstract-Analog synthesis tools have traditionally traded quality for speed, substituting simplified circuit evaluation methods for full simulation in order to accelerate the numerical search for solution candidates. As a result, these tools have failed to migrate into mainstream use primarily because of difficulties in reconciling the simplified models required for synthesis with the industrial-strength simulation environments required for validation. We argue that for synthesis to be practical, it is essential to synthesize a circuit using the same simulation environment created to validate the circuit. In this paper, we develop a new numerical search algorithm efficient enough to allow full circuit simulation of each circuit candidate, and robust enough to find good solutions for difficult circuits. The method combines the population-of-solutions ideas from evolutionary algorithms with a novel variant of pattern search, and supports transparent network parallelism. Comparison of several synthesized cell-level circuits against manual industrial designs demonstrates the utility of the approach.

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ANACONDA: robust synthesis of analog circuits via stochastic pattern search

Cited by 26 publications

References 20 publications

Computer-aided design of analog and mixed-signal integrated circuits

Computer-aided design of analog and mixed-signal integrated circuits

CMOS op-amp sizing using a geometric programming formulation

Anaconda: SimulationBased Synthesis of Analog Circuits Via Stochastic Pattern Search

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