2 citations · 3 across the 5 of their papers we have counts for
5 papers
Improving Multi-Instance GPU Efficiency via Sub-Entry Sharing TLB Design
Bingyao Li, Yueqi Wang, Tianyu Wang +4
NVIDIA's Multi-Instance GPU (MIG) technology enables partitioning GPU computing power and memory into separate hardware instances, providing complete isolation including compute re…
SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing
Sheng Li, Geng Yuan, Yue Dai +3
There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model tr…
BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval
Kaixing Yang, Xukun Zhou, Xulong Tang +4
Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sp…
SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
Zhengang Li, Geng Yuan, Tomoharu Yamauchi +8
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1'…
Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System
Zhendong Wang, Xiaoming Zeng, Xulong Tang +3
The deep neural network (DNN) models are deemed confidential due to their unique value in expensive training efforts, privacy-sensitive training data, and proprietary network chara…