Characterizing and predicting value degree of use
Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
Efficient Microprocessor Design Space Exploration through Statistical Simulation
ANSS '03 Proceedings of the 36th annual symposium on Simulation
Control Flow Modeling in Statistical Simulation for Accurate and Efficient Processor Design Studies
Proceedings of the 31st annual international symposium on Computer architecture
System-level design space exploration for security processor prototyping in analytical approaches
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Microarchitecture evaluation with floorplanning and interconnect pipelining
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Accurate memory data flow modeling in statistical simulation
Proceedings of the 20th annual international conference on Supercomputing
Proceedings of the conference on Design, automation and test in Europe
IEEE Transactions on Computers
A superscalar simulation employing poisson distributed stalls
Computers and Electrical Engineering
Distilling the essence of proprietary workloads into miniature benchmarks
ACM Transactions on Architecture and Code Optimization (TACO)
A mechanistic performance model for superscalar out-of-order processors
ACM Transactions on Computer Systems (TOCS)
Statistical Performance Modeling in Functional Instruction Set Simulators
ACM Transactions on Embedded Computing Systems (TECS)
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Abstract: Microprocessor design time and effort are getting impractical due to the huge number of simulations that need to be done to evaluate various processor configurations for various workloads. An early design stage methodology could be useful to efficiently cull huge design spaces to identify regions of interest to be further explored using more accurate simulations. In this paper, we present an early design stage method that bridges the gap between analytical and statistical modeling. The hybrid analytical-statistical method presented here is based on the observation that register traffic characteristics exhibit power law properties which allows us to fully characterize a workload with just a few parameters which is much more efficient than the collection of distributions that need to be specified in classical statistical modeling. We evaluate the applicability and the usefulness of this hybrid analytical-statistical modeling technique to efficiently and accurately cull huge architectural design spaces. In addition, we demonstrate that this hybrid analytical-statistical modeling technique can be used to explore the entire workload space by varying just a few workload parameters.