28 citations · 58 across the 11 of their papers we have counts for
12 papers
Dynamic Flow, Static Graph: KV Cache Reuse for Efficient LLM Serving on Mobile NPUs
Zhengxiang Huang, Shengheng Chen, Chaoyue Niu +6
On-device large language model (LLM) serving is a cornerstone of local-first personal intelligence, offering users data sovereignty, strong privacy guarantees, and freedom from clo…
HRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone Scans
Chao Shi, Shenghao Jia, Jinhui Liu +6
We present HRMAvatar, a framework for creating high-fidelity avatars from monocular phone scans, which can be rendered and animated in real time on mobile devices. Monocular ca…
MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
Zhengxiang Huang, Chaoyue Niu, Zhaode Wang +8
As the demand for on-device Large Language Model (LLM) inference grows, energy efficiency has become a major concern, especially for battery-limited mobile devices. Our analysis sh…
Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Chaoyue Niu, Yucheng Ding, Junhui Lu +7
The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerg…
Personalized LLM for Generating Customized Responses to the Same Query from Different Users
Hang Zeng, Chaoyue Niu, Fan Wu +2
Existing work on large language model (LLM) personalization assigned different responding roles to LLMs, but overlooked the diversity of queriers. In this work, we propose a new fo…
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
Bowen Cai, Jinchi Huang, Rongfei Jia +2
This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IDR and NeuS overlook the spatial context in 3D space when predicti…
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Chengfei Lv, Chaoyue Niu, Renjie Gu +17
To break the bottlenecks of mainstream cloud-based machine learning (ML) paradigm, we adopt device-cloud collaborative ML and build the first end-to-end and general-purpose system,…
Data-Free Evaluation of User Contributions in Federated Learning
Hongtao Lv, Zhenzhe Zheng, Tie Luo +5
Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device's private data and computing resources. A critical issues is to…
Toward Understanding the Influence of Individual Clients in Federated Learning
Yihao Xue, Chaoyue Niu, Zhenzhe Zheng +4
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guaran…
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices
Yiwu Yao, Yuchao Li, Chengyu Wang +8
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices. In this paper, we present a novel quantized Winograd op…
MNN: A Universal and Efficient Inference Engine
Xiaotang Jiang, Huan Wang, Yiliu Chen +9
Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges o…
Secure Federated Submodel Learning
Chaoyue Niu, Fan Wu, Shaojie Tang +5
Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server.…