A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
Reliability of radial basis function: neural network smart antenna
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Worst-case performance prediction under supply voltage and temperature variation
Proceedings of the 12th ACM/IEEE international workshop on System level interconnect prediction
Improved on-chip router analytical power and area modeling
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
Explicit modeling of control and data for improved NoC router estimation
Proceedings of the 49th Annual Design Automation Conference
Near-threshold voltage (NTV) design: opportunities and challenges
Proceedings of the 49th Annual Design Automation Conference
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Accurate estimators of key design metrics (power, area, delay, etc.) are increasingly required to achieve IC cost reductions in system-level through physical layout optimizations. At the same time, identifying physical or analytical models of design metrics has become very challenging due to interactions among many parameters that span technology, architecture and implementation. Metamodeling techniques can simplify this problem by deriving surrogate models from samples of actual implementation data. However, the use of metamodeling techniques in IC design estimation is still in its infancy, and practitioners need more systematic understanding. In this work, we study the accuracy of metamodeling techniques across several axes: (1) low- and high-dimensional estimation problems, (2) sampling strategies, (3) sample sizes, and (4) accuracy metrics. To help obtain more general conclusions, we study these axes for three very distinct chip design estimation problems: (1) area and power of networks-on-chip routers, (2) delay and output slew of standard cells under power delivery network noise, and (3) wirelength and buffer area of clock trees. Our results show that (1) adaptive sampling can effectively reduce the sample size required to derive surrogate models by up to 64% (or, increase estimation accuracy by up to 77%) compared with Latin hypercube sampling; (2) for low-dimensional problems, Gaussian process-based models can be 1.5x more accurate than tree-based models, whereas for high-dimensional problems, tree-based models can be up to 6x more accurate than Gaussian process-based models; and (3) a variant of weighted surrogate modeling [7], which we call hybrid surrogate modeling, can improve estimation accuracy by up to 3x. Finally, to aid architects, design teams, and CAD developers in selection of the appropriate metamodeling techniques, we propose guidelines based on the insights gained from our studies.