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- W2559346790 abstract "Functional instruction set simulators perform instruction-accurate simulation of benchmarks at high instruction rates. Unlike their slower, but cycle-accurate, counterparts however, they are not capable of providing cycle counts due to the higher level of hardware abstraction. In this report we present a novel approach to performance prediction based on statistical machine learning utilising a hybrid functional and cycle-accurate simulator. We introduce the concept of continuous learning to simulation whereby new training data points are acquired on demand and used for on-the-fly updates of the prediction model. Furthermore, we show how statistical regression can be adapted to reduce the cost of these updates during a performance-critical simulation. For a stateof-the-art simulator modelling the ARC 750D embedded processor core we demonstrate that our approach is highly accurate, with average error < 2.5% whilst achieving a speed-up of ≈ 1.5 times that of the baseline cycle-accurate simulation. When extended to use a modern JIT-compiled simulation for the functional simulation, this would enable the full simulation of e.g. complex multimedia applications." @default.
- W2559346790 created "2016-12-08" @default.
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- W2559346790 date "2009-01-01" @default.
- W2559346790 modified "2023-09-23" @default.
- W2559346790 title "Using Continuous Statistical Machine Learning to Enable Performance Prediction in Hybrid Functional/Cycle Accurate Instruction Set Simulators" @default.
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