12 citations · 43 across the 16 of their papers we have counts for
16 papers
Characterizing and Detecting WebAssembly Runtime Bugs
Yixuan Zhang, Shangtong Cao, Haoyu Wang +6
WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and deskto…
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning
Jialiang Han, Yudong Han, Gang Huang +1
Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a small scale of orga…
Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading
Daliang Xu, Mengwei Xu, Qipeng Wang +6
This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal…
Benchmarking of DL Libraries and Models on Mobile Devices
Qiyang Zhang, Xiang Li, Xiangying Che +6
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms…
Rise of Distributed Deep Learning Training in the Big Model Era: From a Software Engineering Perspective
Xuanzhe Liu, Diandian Gu, Zhenpeng Chen +5
Deep learning (DL) has become a key component of modern software. In the "big model" era, the rich features of DL-based software substantially rely on powerful DL models, e.g., BER…
: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
Jialiang Han, Yun Ma
Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal rec…