FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

1 code implementation • 2 Aug 2023 • Tengju Ye, Wei Jing, Chunyong Hu, Shikun Huang, Lingping Gao, Fangzhen Li, Jingke Wang, Ke Guo, Wencong Xiao, Weibo Mao, Hang Zheng, Kun Li, Junbo Chen, Kaicheng Yu

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving.

Autonomous Driving

MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs

1 code implementation • 1 Jan 2023 • Huaizheng Zhang, Yuanming Li, Wencong Xiao, Yizheng Huang, Xing Di, Jianxiong Yin, Simon See, Yong Luo, Chiew Tong Lau, Yang You

The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts.

Benchmarking

Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads

1 code implementation • 17 Jan 2019 • Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang

With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products.

Distributed, Parallel, and Cluster Computing

Balanced Sparsity for Efficient DNN Inference on GPU

no code implementations • 1 Nov 2018 • Zhuliang Yao, Shijie Cao, Wencong Xiao, Chen Zhang, Lanshun Nie

However, it requires the customization of hardwares to speed up practical inference.