5 citations · 8 across the 6 of their papers we have counts for
6 papers
CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers
Zhuojin Li, Hsin-Pai Cheng, Hong Cai +2
Diffusion Transformers (DiTs) deliver remarkable image and video generation quality but incur high computational cost, limiting scalability and on-device deployment. We introduce C…
A Study on Inference Latency for Vision Transformers on Mobile Devices
Zhuojin Li, Marco Paolieri, Leana Golubchik
Given the significant advances in machine learning techniques on mobile devices, particularly in the domain of computer vision, in this work we quantitatively study the performance…
Accelerating Mobile Inference through Fine-Grained CPU-GPU Co-Execution
Zhuojin Li, Marco Paolieri, Leana Golubchik
Deploying deep neural networks on mobile devices is increasingly important but remains challenging due to limited computing resources. On the other hand, their unified memory archi…
Inference Latency Prediction at the Edge
Zhuojin Li, Marco Paolieri, Leana Golubchik
With the growing workload of inference tasks on mobile devices, state-of-the-art neural architectures (NAs) are typically designed through Neural Architecture Search (NAS) to ident…
Galleon: Reshaping the Square Peg of NFV
Jianfeng Wang, Tamás Lévai, Zhuojin Li +3
Software is often used for Network Functions (NFs) -- such as firewalls, NAT, deep packet inspection, and encryption -- that are applied to traffic in the network. The community ha…
Throughput Prediction of Asynchronous SGD in TensorFlow
Zhuojin Li, Wumo Yan, Marco Paolieri +1
Modern machine learning frameworks can train neural networks using multiple nodes in parallel, each computing parameter updates with stochastic gradient descent (SGD) and sharing t…