Energy-Efficient Deep Learning-Based Image Compression for LPWANs on Resource-Constrained AIoT Devices
MEMBER IN THE JOINT ACADEMIC PARTNERSHIP
PD Dr.-Ing. Daniel Müller-Gritschneder
- Entwurfsmethoden für eingebettete Systeme
- Edge Machine Learning (TinyML)
Hochschule für angewandte Wissenschaften Landshut
Recently there has been a rising demand for Low-Power Wide-Area Network (LPWAN)-based computer vision applications. LPWANs are speciﬁcally designed for long battery life, high transmission range and low production costs, which come however at the expense of very low bandwidths. Consequently, data compression plays a crucial role for energy-eﬃcient image communication, due to the signiﬁcantly higher energy costs associated with communication compared to computation. For that, novel compute-intensive compression methods, like deep-learning based image compression techniques, are needed/promising to further reduce the number of packets to be transmitted. Despite their superiority over hitherto established methods, such as JPEG, there is no related research that jointly addresses deep learning-based compression performance and resource eﬃciency on sensor platforms. Only recently, high computational power at low battery consumption has become possible, by exploiting parallel ultra low power processors like GAP8. The goal of this research is to develop robust, energy-eﬃcient deep learning-based image compression techniques for LPWANs on resource-constrained AI-enabled IoT (AIoT) devices. Having higher compression rates while operating at low-power will dramatically reduce network traﬃc (especially in case of low-bandwidths), extend battery life of visual IoT sensor nodes and pave the way to a broad range of new data-intensive applications within the LPWAN/ 5G mMTC communication era.