Statistical estimation of leakage current considering inter- and intra-die process variation
Proceedings of the 2003 international symposium on Low power electronics and design
Statistical Timing Analysis for Intra-Die Process Variations with Spatial Correlations
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Modeling Within-Die Spatial Correlation Effects for Process-Design Co-Optimization
ISQED '05 Proceedings of the 6th International Symposium on Quality of Electronic Design
Variability and energy awareness: a microarchitecture-level perspective
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 42nd annual Design Automation Conference
Performance and yield enhancement of FPGAs with within-die variation using multiple configurations
Proceedings of the 2007 ACM/SIGDA 15th international symposium on Field programmable gate arrays
Parametric yield in FPGAs due to within-die delay variations: a quantitative analysis
Proceedings of the 2007 ACM/SIGDA 15th international symposium on Field programmable gate arrays
Microarchitecture parameter selection to optimize system performance under process variation
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
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The focus of this paper is to measure and qualify high-level process variation models by measuring variability on FPGAs. Measurements are done with high spatial resolution and demonstrate how the high-resolution data matches two industry test cases. The benefit of such an approach is that several inexpensive FPGAs, which are normally on the leading edge of technologies compared to ASICs, obviate the need of fabricating many custom test chips. Specifically, our evaluation shows how measurements of an Altera Cyclone II FPGA can be used to derive variability models for several 90nm commercial designs such as the Sun Niagara and Intel Pentium D. Even though the FPGAs and commercial processors are produced by different fabs (TSMC, TI, and Intel, respectively), we find the FPGAs to be very useful for predicting variation in the commercial processors.