Self-Adaptive Approximate Mobile Deep Learning

Electronics

Octavian Machidon¹  Timotej Knez¹  Veljko Pejović¹ 

[1] Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia;

关键词: mobile sensing;    neural networks;    dynamic optimization;    quantization;    DNN slimming;   

DOI  :  10.3390/electronics10232958

来源: DOAJ

【 摘 要 】

Edge intelligence is currently facing several important challenges hindering its performance, with the major drawback being meeting the high resource requirements of deep learning by the resource-constrained edge computing devices. The most recent adaptive neural network compression techniques demonstrated, in theory, the potential to facilitate the flexible deployment of deep learning models in real-world applications. However, their actual suitability and performance in ubiquitous or edge computing applications has not, to this date, been evaluated. In this context, our work aims to bridge the gap between the theoretical resource savings promised by such approaches and the requirements of a real-world mobile application by introducing algorithms that dynamically guide the compression rate of a neural network according to the continuously changing context in which the mobile computation is taking place. Through an in-depth trace-based investigation, we confirm the feasibility of our adaptation algorithms in offering a scalable trade-off between the inference accuracy and resource usage. We then implement our approach on real-world edge devices and, through a human activity recognition application, confirm that it offers efficient neural network compression adaptation in highly dynamic environments. The results of our experiment with 21 participants show that, compared to using static network compression, our approach uses 2.18× less energy with only a 1.5% drop in the average accuracy of the classification.

【 授权许可】

Unknown