37 citations · 50 across the 8 of their papers we have counts for
8 papers
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Xinyu Liu, Houwen Peng, Ningxin Zheng +3
Vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them…
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
Li Lyna Zhang, Xudong Wang, Jiahang Xu +6
The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, direc…
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Guanghao Yin, Zefan Qu, Xinyang Jiang +8
Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applyin…
PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation
Ningxin Zheng, Huiqiang Jiang, Quanlu Zhang +8
Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions…
Online Video Super-Resolution with Convolutional Kernel Bypass Graft
Jun Xiao, Xinyang Jiang, Ningxin Zheng +5
Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) in recent years, but most of these models are less applicable to online video applic…
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
Bo Li, Xinyang Jiang, Donglin Bai +6
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of…
Towards QoS-Aware and Resource-Efficient GPU Microservices Based on Spatial Multitasking GPUs In Datacenters
Wei Zhang, Quan Chen, Kaihua Fu +6
While prior researches focus on CPU-based microservices, they are not applicable for GPU-based microservices due to the different contention patterns. It is challenging to optimize…
URSA: Precise Capacity Planning and Contention-aware Scheduling for Public Clouds
Ningxin Zheng, Quan Chen, Yong Yang +4
Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clo…