Memory Architectures for Embedded Binarized Neural Networks on IoT Devices in Low Voltage Environment

• Main Authors: Syu, shu-Wei, 許書維
• Other Authors: Chen, Tien-Fu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/q7fj65

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === In the future of IoT, IoT devices will not only perform data collection and filtering, but execute some simple applications, such as motion detection, behavior identification, etc. due to the complexity of the data collected by the sensor nodes in the future. It is too difficult to analyze with human ability. So machine learning will be needed in the future networking to assist in the analysis. Current IoT device is working in the low voltage environment in general; implement the artificial neural network module to such identification will cost additional energy consumption. We attempt to find the methodology that can reduce energy consumption of IoT device in low voltage environment. After analyzed the neural network model (eBNN) on IoT device, we find out most of energy originated from long memory access time in low voltage. However, dominator is not the memory; the dominator is other component that have to wait the data to be read back from memory. We refer the real IoT device chip LPC18A1. In addition, proposed two methodologies, which are buffer control unit and pre-fetch unit to solve this issue. Although the energy consumption of the memory is slightly increased, but the total energy consumption of the IoT device can be reduced along with execution time.