부산대학교 도서관

With more powerful yet efficient embedded devices and accelerators being available for Deep Neural Networks (DNN), machine learning is becoming an integral part of edge computing. As the number of such devices increases, finding the best platform for a specific application has become more challenging. A common question for application developers is to find the most cost-effective combination of a DNN and a device while still meeting latency and accuracy requirements. In this work, we propose Blackthorn, a layer-wise latency estimation framework for embedded Nvidia GPUs based on analytical models. We provide accurate predictions for each layer, helping developers to find bottlenecks and optimize the architecture of a DNN to fit target platforms. Our framework can quickly evaluate and compare large amounts of network optimizations without needing to build time-consuming execution engines. Our experimental results on Jetson TX2 and Jetson Nano devices show a per-layer estimation error of 6.104% Root-Mean-Square-Percentage-Error (RMSPE) and 5.888% RMSPE, which significantly outperforms current state-of-the-art methods. At network level, the average latency error is below 3% for the tested DNNs.