High-Performance Edge Computing: An Energy Data Lakes Case Study

With the varying applications of Artificial Intelligence (AI) on an ever expanding global scale, performance metrics and standards of computing platforms have steady elevated. High-Performance Edge Computing (HPEC) can play an instrumental role in lifting a substantial load on cloud computing solutions when it comes to deploying Deep Learning (DL) algorithms for classifying big data. Notwithstanding, the collection, pre-processing, and post-processing of big data engenders many challenges and opportunities for optimizing HPEC performance for data classification and, in turn, yield better outcomes. This is where the concept of data lakes is employed, which are raw-formatted large masses of data that are plausibly more compatible with many algorithms than structured data stores. Therefore, in this book chapter, we aim to carry out a comparative study that examines the merits, performance and efficiency metrics, and limitations of two notable HPECs centered around a DL data lake classification framework. Comparing the HPEC platforms, with similar classification accuracy of ~90%, results show that adequate performance and impressive computational efficiency, as fast as 8.19 msec per classified GAF image is achieved on the Jetson Nano.