185 citations · 778 across the 44 of their papers we have counts for
44 papers
ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference
Wangsong Yin, Daliang Xu, Mengwei Xu +2
On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention operator falls back from the special-pu…
Elastic On-Device LLM Service
Wangsong Yin, Rongjie Yi, Daliang Xu +3
On-device Large Language Models (LLMs) are transforming mobile AI, catalyzing applications like UI automation without privacy concerns. Nowadays the common practice is to deploy a…
Fast On-device LLM Inference with NPUs
Daliang Xu, Hao Zhang, Liming Yang +4
On-device inference for Large Language Models (LLMs), driven by increasing privacy concerns and advancements of mobile-sized models, has gained significant interest. However, even…
Research on WebAssembly Runtimes: A Survey
Yixuan Zhang, Mugeng Liu, Haoyu Wang +3
WebAssembly (abbreviated as Wasm) was initially introduced for the Web but quickly extended its reach into various domains beyond the Web. To create Wasm applications, developers c…
SCOPE: Performance Testing for Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Jianshu Zhao +5
Serverless computing is a popular cloud computing paradigm that has found widespread adoption across various online workloads. It allows software engineers to develop cloud applica…
Energy-Efficient GPU Clusters Scheduling for Deep Learning
Diandian Gu, Xintong Xie, Gang Huang +2
Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy c…
Fast Distributed Inference Serving for Large Language Models
Bingyang Wu, Yinmin Zhong, Zili Zhang +6
Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT. The interactive nature of these applications demands low latency for LLM…
Unveiling Overlooked Performance Variance in Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Federica Sarro +1
Serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution e…
MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
Yihao Zhao, Xin Liu, Shufan Liu +5
Large-scale GPU clusters are widely-used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, most DL clusters either dedicat…
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…
Software Engineering for Serverless Computing
Jinfeng Wen, Zhenpeng Chen, Xuanzhe Liu
Serverless computing is an emerging cloud computing paradigm that has been applied to various domains, including machine learning, scientific computing, video processing, etc. To d…
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…
FaaSLight: General Application-Level Cold-Start Latency Optimization for Function-as-a-Service in Serverless Computing
Xuanzhe Liu, Jinfeng Wen, Zhenpeng Chen +5
Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverle…
Rise of the Planet of Serverless Computing: A Systematic Review
Jinfeng Wen, Zhenpeng Chen, Xin Jin +1
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications. It allows developers to focus on the application logic…
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…
From Cloud to Edge: A First Look at Public Edge Platforms
Mengwei Xu, Zhe Fu, Xiao Ma +7
Public edge platforms have drawn increasing attention from both academia and industry. In this study, we perform a first-of-its-kind measurement study on a leading public edge plat…
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
Zhenpeng Chen, Huihan Yao, Yiling Lou +4
Deep Learning (DL) is finding its way into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobi…
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
Xuan Lu, Wei Ai, Zhenpeng Chen +2
Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more em…
VM Matters: A Comparison of WASM VMs and EVMs in the Performance of Blockchain Smart Contracts
Shuyu Zheng, Haoyu Wang, Lei Wu +2
WebAssemly is an emerging runtime for Web applications and has been supported in almost all browsers. Recently, WebAssembly is further regarded to be a the next-generation environm…
Hierarchical Federated Learning through LAN-WAN Orchestration
Jinliang Yuan, Mengwei Xu, Xiao Ma +3
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL pro…
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
Leye Wang, Di Chai, Xuanzhe Liu +2
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent dee…
Don't Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams
Pengcheng Xia, Haoyu Wang, Xiapu Luo +6
As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency sca…
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
Chengxu Yang, Qipeng Wang, Mengwei Xu +4
Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry. A unique characteristic of FL is h…