Jonghun Jeong  Dasom Lee  Hyeonseok Jung  Hoeseok Yang

10.5626/JOK.2020.47.2.136

internet-of-things device Embedded System microprocessor Neural Network neural network compression filter pruning

Recently, attempts have been made to directly execute various convolutional neural network applications in resource-constrained embedded systems such as IoT. However, since embedded systems have limited computational capability and memory, the size of the neural network model that can be executed is restricted and may not satisfy real-time constraints. Therefore, in this paper, we propose a framework that automatically compresses a given neural network model to satisfy memory and execution time requirements and automatically generates code that can be executed on the target embedded system. Using the proposed framework, we demonstrate that the given neural network models can be automatically optimized for two STM32 Nucleo series boards with different HW specifications for various execution time and memory requirements.