Object detection is a fundamental building block of video analytics applications. While Neural Networks (NNs)-based object detection models have shown excellent accuracy on …
Concurrent inference execution on heterogeneous processors is critical to improve the performance of increasingly heavy deep learning (DL) models. However, available …
R Liang, T Cao, J Wen, M Wang, Y Wang… - Proceedings of the 28th …, 2022 - dl.acm.org
Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and …
Mobile devices are increasingly equipped with heterogeneous multiprocessors, eg, CPU+ GPU+ DSP. Yet existing Neural Network (NN) inference fails to fully utilize the computing …
J Lee, Y Liu, Y Lee - Proceedings of the 5th International Workshop on …, 2021 - dl.acm.org
Mobile GPUs are extremely under-utilized for DNN computations across different mobile deep learning frameworks and multiple DNNs with various complexities. We explore the …
More specialized chips are exploiting available high transistor density to expose parallelism at a large scale with more intricate instruction sets. This paper reports on a compilation …
K Wang, J Cao, Z Zhou, Z Li - IEEE Transactions on Mobile …, 2024 - ieeexplore.ieee.org
Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables various autonomous mobile computing applications. However, the memory budget of edge …
Y Wan, M Liu, G Li, F Dong - 2023 IEEE 29th International …, 2023 - ieeexplore.ieee.org
Object detection, image classification, and various other computer vision tasks have become prevalent on mobile devices. These computer vision tasks are typically executed with three …