Hybrid Performance Prediction Models for Fully-Connected Neural Networks on MPSoC
Quentin Dariol · elib (German Aerospace Center) · 2022
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.