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Noise Considerations in Circuit Optimization
 In Proc. International Conference on ComputerAided Design
, 1998
"... Noise can cause digital circuits to switch incorrectly and thus produce spurious results. Noise can also have adverse power, timing and reliability e ects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise ..."
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Cited by 13 (0 self)
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Noise can cause digital circuits to switch incorrectly and thus produce spurious results. Noise can also have adverse power, timing and reliability e ects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semiin nite constraints. In addition, the number of signals to be checked and the number of subintervals of time during which the checking must be performed can potentially be very large. Thus, the practical incorporation of noise constraints during circuit optimization is a hitherto unsolved problem. This paper describes a novel method for incorporating noise considerations during automatic circuit optimization. Semiin nite constraints representing noise considerations are rst converted toordinary equality constraints involving time integrals, which are readily computed in the context of circuit optimization based on timedomain simulation. Next, the gradients of these integrals are computed by the adjoint method. By using an augmented Lagrangian optimization merit function, the adjoint method is applied tocompute all the necessary gradients required for optimization in a single adjoint analysis, no matter how many noise measurements are considered and irrespective of the dimensionality of the problem. Numerical results are presented. 1
Optimization of Custom MOS Circuits by Transistor Sizing
 IEEE INTERNATIONAL CONFERENCE ON COMPUTERAIDED DESIGN
, 1996
"... Optimization of a circuit by transistor sizing is often a slow, tedious and iterative manual process which relies on designer intuition. Circuit simulation is carried out in the inner loop of this tuning procedure. Automating the transistor sizing process is an important step towards being able to r ..."
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Cited by 13 (5 self)
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Optimization of a circuit by transistor sizing is often a slow, tedious and iterative manual process which relies on designer intuition. Circuit simulation is carried out in the inner loop of this tuning procedure. Automating the transistor sizing process is an important step towards being able to rapidly design highperformance, custom circuits. JiffyTune is a new circuit optimization tool that automates the tuning task. Delay, rise/fall time, area and power targets are accommodated. Each (weighted) target can be either a constraint or an objective function. Minimax optimization is supported. Transistors can be ratioed and similar structures grouped to ensure regular layouts. Bounds on transistor widths are supported. JiffyTune uses
Optimization techniques for highperformance digital circuits
 in Proc. IEEE Int. Conf. ComputerAided Design (ICCAD
, 1997
"... The relentless push for high performance in custom digital circuits has led to renewed emphasis on circuit optimization or tuning. The parameters of the optimization are typically transistor and interconnect sizes. The design metrics are not just delay, transition times, power and area, but also ..."
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Cited by 11 (2 self)
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The relentless push for high performance in custom digital circuits has led to renewed emphasis on circuit optimization or tuning. The parameters of the optimization are typically transistor and interconnect sizes. The design metrics are not just delay, transition times, power and area, but also signal integrity and manufacturability. This tutorial paper discusses some of the recently proposed methods of circuit optimization, with an emphasis on practical application and methodology impact. Circuit optimization techniques fall into three broad categories. The rst is dynamic tuning, based on timedomain simulation of the underlying circuit, typically combined with adjoint sensitivity computation. These methods are accurate but require the specication of input signals, and are best applied to small data
ow circuits and \crosssections " of larger circuits. Ecient sensitivity computation renders feasible the tuning of circuits with a few thousand transistors. Second, static tuners employ static timing analysis to evaluate the performance of the circuit. All paths through the logic are simultaneously tuned, and no input vectors are required. Large control macros are best tuned by these methods. However, in the context of deep submicron custom design, the inaccuracy of the delay models employed by these methods often limits their utility. Aggressive dynamic or static tuning can push a circuit into a precipitous corner of the manufacturing process space, which is a problem addressed by the third class of circuit optimization tools, statistical tuners. Statistical techniques are used to enhance manufacturability or maximize yield. In addition to surveying the above techniques, topics such as the use of stateoftheart nonlinear optimization methods and special considerations for interconnect sizing, clock tree optimization and noiseaware tuning will be brie
y considered. 1
Circuit Optimization via Adjoint Lagrangians
 IEEE INTERNATIONAL CONFERENCE ON COMPUTERAIDED DESIGN
, 1997
"... The circuit tuning problem is best approached by means of gradientbased nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable paramete ..."
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Cited by 8 (4 self)
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The circuit tuning problem is best approached by means of gradientbased nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable parameters, the direct method [2] is used to repeatedly solve the associated sensitivity circuit to obtain all the necessary gradients. Likewise, when the parameters outnumber the measurements, the adjoint method [1] is employed to solve the adjoint circuit repeatedly for each measurement to compute the sensitivities. In this paper, we propose the adjoint Lagrangian method, which computes all the gradients necessary for augmentedLagrangianbased optimization in a single adjoint analysis. After the nominal simulation of the circuit has been carried out, the gradients of the merit function are expressed as the gradients of a weighted sum of circuit measurements. The weights are dependent on the nominal solution and on optimizer quantities such as Lagrange multipliers. By suitably choosing the excitations of the adjoint circuit, the gradients of the merit function are computed via a single adjoint analysis, irrespective of the number of measurements and the number of parameters of the optimization. This procedure requires close integration between the nonlinear optimization software and the circuit simulation program. The adjoint
Noise Considerations in . . .
"... Noise can cause digital circuits to switch incorrectly, producing spurious results. It can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise consider ..."
Abstract
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Noise can cause digital circuits to switch incorrectly, producing spurious results. It can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to chargesharing and coupling noise. Thus the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semiinfinite constraints in the timedomain. Semiinfinite problems are generally harder to solve than standard nonlinear optimization problems. Moreover, the number of noise constraints can potentially be very large. This paper