PFS - Using ML for Back-Annotating Low-Level Effects in a System-Level Framework
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hdl:2117/369970
Tipus de documentText en actes de congrés
Data publicació2022-05
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Abstract
The work presented here is on back-annotation of
physical properties into components of an embedded system for
system-level simulation and facilitating fast design space
exploration. Two important properties, power and noise are
considered in this thesis. A general ML-based framework is
proposed for both power and noise back-annotation. This
framework removes the expensive low-level simulations and
performs a one-time offline effort for low-level property
characterization. The actual data from characterization generates
the dataset for training ML-based models. We evaluate several
ML techniques on this task like multi-layer perceptron,
convolutional neural network, gradient boosting, and LSTM. The
trained model will be implemented in a fast SystemC surrogate
model that speeds up the simulation time for system design space
exploration. In this paper the methodology will be explained for
interconnect crosstalk back-annotation. A crosstalk model for a
RISC-V like processor interconnects is implemented and
evaluated as a case study. The same methodology is used for backannotating
and evaluating processor power at system level.
CitacióBasharkhah, K.; Navabi, Z. Using ML for Back-Annotating Low-Level Effects in a System-Level Framework. A: 27th IEEE European Test Symposium (ETS). 2022,
Versió de l'editorhttps://ieeexplore.ieee.org/xpl/conhome/9810327/proceeding
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