Search Results for author: Zihan Jiang

Found 12 papers, 0 papers with code

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

no code implementations13 Sep 2023 YuanHao Liu, Dehui Du, Zihan Jiang, Anyan Huang, Yiyang Li

To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.

Causal Inference Time Series +1

CMLCompiler: A Unified Compiler for Classical Machine Learning

no code implementations31 Jan 2023 Xu Wen, Wanling Gao, Anzheng Li, Lei Wang, Zihan Jiang, Jianfeng Zhan

Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues.

Pinpointing the Memory Behaviors of DNN Training

no code implementations1 Apr 2021 Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.

HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking

no code implementations25 Feb 2021 Zihan Jiang, Wanling Gao, Fei Tang, Xingwang Xiong, Lei Wang, Chuanxin Lan, Chunjie Luo, Hongxiao Li, Jianfeng Zhan

Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.

Image Classification Performance

AIBench Scenario: Scenario-distilling AI Benchmarking

no code implementations6 May 2020 Wanling Gao, Fei Tang, Jianfeng Zhan, Xu Wen, Lei Wang, Zheng Cao, Chuanxin Lan, Chunjie Luo, Xiaoli Liu, Zihan Jiang

We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.


The Pitfall of Evaluating Performance on Emerging AI Accelerators

no code implementations8 Nov 2019 Zihan Jiang, Jiansong Li, Jiangfeng Zhan

To reveal this pitfall, we evaluates several frequently-used optimizations on a typical AI accelerator and quantifies their impact on accuracy and throughout under representative DL inference workloads.

HPC AI500: A Benchmark Suite for HPC AI Systems

no code implementations27 Jul 2019 Zihan Jiang, Wanling Gao, Lei Wang, Xingwang Xiong, Yuchen Zhang, Xu Wen, Chunjie Luo, Hainan Ye, Yunquan Zhang, Shengzhong Feng, Kenli Li, Weijia Xu, Jianfeng Zhan

In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.

A Semantic-based Medical Image Fusion Approach

no code implementations1 Jun 2019 Fanda Fan, Yunyou Huang, Lei Wang, Xingwang Xiong, Zihan Jiang, Zhifei Zhang, Jianfeng Zhan

Medical image fusion is a promising approach to providing overall information from medical images of different modalities.

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