Hybrid Performance Prediction Models for Fully-Connected Neural Networks on MPSoC
Résumé
Predicting the performance of Artificial Neural
Networks (ANNs) on embedded multi-core platforms is tedious.
Concurrent accesses to shared resources are hard to model due
to congestion effects on the shared communication medium,
which affect the performance of the application. In this paper
we present a hybrid modeling environment to enable fast yet
accurate timing prediction for fully-connected ANNs deployed
on multi-core platforms. The modeling flow is based on the
integration of an analytical computation time model with a
communication time model which are both calibrated through
measurement inside a system level simulation using SystemC. The
proposed flow enables the prediction of the end-to-end latency
for different mappings of several fully-connected ANNs with an
average of more than 99 % accuracy.
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