Recurrent Neural Networks Analysis for Embedded Systems

Abstract

Artificial Neural Networks (ANNs) are biologically inspired algorithms especially efficient for pattern recog- nition and data classification. In particular, Recurrent Neural Networks (RNN) are a specific type of ANNs which model and process sequences of data that have temporal relationship. Thus, it introduces interesting behavior for embedded systems applications such as autopilot systems. However, RNNs (and ANNs in gen- eral) are computationally intensive algorithms, especially to allow the network to learn. This implies a wise integration and proper analysis on the embedded systems that we gather these functionalities. We present in this paper an analysis of two types of Recurrent Neural Networks, Long-Short Term Memory (LSTM) and Gated-Recurrent Unit (GRU), explain their architectures and characteristics. We propose our dedicated implementation which is tested and validated on embedded system devices with a dedicated dataset.