A Hybrid Performance Prediction Approach for Fully-Connected Artificial Neural Networks on Multi-core Platforms

Abstract

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. Most approaches focus therefore on evaluation through systematic implementation and testing or through the building of analytical models, which tend to lack of accuracy when targeting a wide range of architectures of varying complexity. 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 ANN is described using the Synchronous DataFlow (SDF) Model of Computation (MoC), which offers a strict separation of communications and computations and thus enables the building of separated computation and communication time models. The proposed flow enables the prediction of the end-to-end latency for different mappings of several fully-connected ANNs with an average of 99,5% accuracy between the created models and real implementation.