Search Results for author: Sian Jin

Found 9 papers, 3 papers with code

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

no code implementations1 Nov 2022 Baixi Sun, Xiaodong Yu, Chengming Zhang, Jiannan Tian, Sian Jin, Kamil Iskra, Tao Zhou, Tekin Bicer, Pete Beckman, Dingwen Tao

Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.

Efficient PHY Layer Abstraction under Imperfect Channel Estimation

no code implementations22 May 2022 Liu Cao, Lyutianyang Zhang, Sian Jin, Sumit Roy

As most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, it may become unreliable if there exists channel estimation error in a real communication system.

COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

1 code implementation18 Nov 2021 Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.

Data Compression

Exploring Autoencoder-based Error-bounded Compression for Scientific Data

no code implementations25 May 2021 Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello

(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.

Image Compression

Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather

no code implementations4 Apr 2021 Xiangyu Gao, Sumit Roy, Guanbin Xing, Sian Jin

Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions.

ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning

no code implementations20 Nov 2020 Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, Dingwen Tao

Moreover, compared with the state-of-the-art pruning-during-training approach, ClickTrain provides significant improvements both accuracy and compression ratio on the tested CNN models and datasets, under similar limited training time.

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

no code implementations18 Nov 2020 Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks.

cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

2 code implementations19 Jul 2020 Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, Franck Cappello

To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.

Distributed, Parallel, and Cluster Computing

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

1 code implementation26 Jan 2019 Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello

In this paper, we propose DeepSZ: an accuracy-loss bounded neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time.

 Ranked #1 on Neural Network Compression on ImageNet (using extra training data)

Network Pruning Neural Network Compression

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