arXiv subjects

Jianxin Liao

Publications and source records attributed to Jianxin Liao.

21 records · Page 2Linked to original sources

arXiv2023

Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource

Deep Neural Networks (DNNs) have significantly improved the accuracy of intelligent applications on mobile devices. DNN surgery, which partitions DNN processing between mobile devices and multi-access edge computing (MEC) servers, can enable real-time inference despite the computational limitations of mobile devices. However, DNN surgery faces a critical challenge: determining the optimal computing resource demand from the server and the corresponding partition strategy, while considering both inference latency and MEC server usage costs. This problem is compounded by two factors: (1) the finite computing capacity of the MEC server, which is shared among multiple devices, leading to inter-dependent demands, and (2) the shift in modern DNN architecture from chains to directed acyclic graphs (DAGs), which complicates potential solutions. In this paper, we introduce a novel Decentralized DNN Surgery (DDS) framework. We formulate the partition strategy as a min-cut and propose a resource allocation game to adaptively schedule the demands of mobile devices in an MEC environment. We prove the existence of a Nash Equilibrium (NE), and develop an iterative algorithm to efficiently reach the NE for each device. Our extensive experiments demonstrate that DDS can effectively handle varying MEC scenarios, achieving up to 1.25$\times$ acceleration compared to the state-of-the-art algorithm.

cs.GT↗

arXiv2022

Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding

Temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. However, recent works find that existing methods suffer a severe temporal bias problem. These methods do not reason the target moment locations based on the visual-textual semantic alignment but over-rely on the temporal biases of queries in training sets. To this end, this paper proposes a novel training framework for grounding models to use shuffled videos to address temporal bias problem without losing grounding accuracy. Our framework introduces two auxiliary tasks, cross-modal matching and temporal order discrimination, to promote the grounding model training. The cross-modal matching task leverages the content consistency between shuffled and original videos to force the grounding model to mine visual contents to semantically match queries. The temporal order discrimination task leverages the difference in temporal order to strengthen the understanding of long-term temporal contexts. Extensive experiments on Charades-STA and ActivityNet Captions demonstrate the effectiveness of our method for mitigating the reliance on temporal biases and strengthening the model's generalization ability against the different temporal distributions. Code is available at https://github.com/haojc/ShufflingVideosForTSG.

cs.CV↗

arXiv2013

Loss Rate Based Fountain Codes for Data Transfer

Fountain codes are becoming increasingly important for data transferring over dedicated high-speed long-distance network. However, the encoding and decoding complexity of traditional fountain codes such as LT and Raptor codes are still high. In this paper, a new fountain codes named LRF (Loss Rate Based Fountain) codes for data transfer is proposed. In order to improve the performance of encoding and decoding efficiency and decrease the number of redundant encoding symbols, an innovative degree distribution instead of robust soliton degree distribution in LT (Luby Transfer) codes is proposed. In LRF codes, the degree of encoding symbol is decided by loss rate property, and the window size is extended dynamic. Simulations result using LRF codes show that the proposed method has better performance in term of encoding ratio, degree ratio, encoding and decoding efficiency with respect to LT and Raptor codes.

cs.NI↗