32 citations · 61 across the 10 of their papers we have counts for
11 papers
Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data
Qiyang Zhang, Xinhao Li, Lei Shi +4
Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, updating large-scale model para…
AI Infrastructure in Space: How Far Can We Go?
Qing Li, Qiyang Zhang, Daliang Xu +10
Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain dep…
Clear-Weighted Bit Allocation for Satellite Downlinks
Alireza Furutanpey, Qiyang Zhang, Yujie Huang +2
Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard frames or tiles, and compress…
Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution
Yunquan Gao, Zhiguo Zhang, Praveen Kumar Donta +4
Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving demand for mobile device support. However, existing mobile inference frameworks often rely…
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Alireza Furutanpey, Qiyang Zhang, Philipp Raith +3
Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, ne…
Resource-efficient In-orbit Detection of Earth Objects
Qiyang Zhang, Xin Yuan, Ruolin Xing +6
With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for proc…
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Mengwei Xu, Wangsong Yin, Dongqi Cai +15
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine lear…
Energy and Time-Aware Inference Offloading for DNN-based Applications in LEO Satellites
Yijie Chen, Qiyang Zhang, Yiran Zhang +2
In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology f…
The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites
Shangguang Wang, Qiyang Zhang, Ruolin Xing +2
Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit. However, the…
Collaborative Inference in DNN-based Satellite Systems with Dynamic Task Streams
Jinglong Guan, Qiyang Zhang, Ilir Murturi +3
As a driving force in the advancement of intelligent in-orbit applications, DNN models have been gradually integrated into satellites, producing daily latency-constraint and comput…
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…