Sheng Lin

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Northeastern University ∙ Zhejiang University ∙ Syracuse University

ESMFL: Efficient and Secure Models for Federated Learning

Deep Neural Networks are widely applied to various domains. The successf...
09/03/2020 ∙ by Sheng Lin, et al. ∙ 0 ∙ share

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RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

Recurrent neural networks (RNNs) based automatic speech recognition has ...
02/19/2020 ∙ by Peiyan Dong, et al. ∙ 0 ∙ share

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An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Weight pruning has been widely acknowledged as a straightforward and eff...
01/20/2020 ∙ by Xiaolong Ma, et al. ∙ 18 ∙ share

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PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

With the emergence of a spectrum of high-end mobile devices, many applic...
01/01/2020 ∙ by Wei Niu, et al. ∙ 15 ∙ share

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A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation

The computing wall and data movement challenges of deep neural networks ...
11/24/2019 ∙ by Geng Yuan, et al. ∙ 0 ∙ share

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DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks

The rapidly growing parameter volume of deep neural networks (DNNs) hind...
11/19/2019 ∙ by Ao Ren, et al. ∙ 0 ∙ share

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Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

Deep neural networks (DNNs) have been expanded into medical fields and t...
11/04/2019 ∙ by Hongjia Li, et al. ∙ 0 ∙ share

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Learning Dynamic Context Augmentation for Global Entity Linking

Despite of the recent success of collective entity linking (EL) methods,...
09/04/2019 ∙ by Xiyuan Yang, et al. ∙ 0 ∙ share

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An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM

The high computation and memory storage of large deep neural networks (D...
08/29/2019 ∙ by Geng Yuan, et al. ∙ 8 ∙ share

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Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation

The state-of-art DNN structures involve intensive computation and high m...
08/27/2019 ∙ by Xiaolong Ma, et al. ∙ 19 ∙ share

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Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy ...
07/03/2019 ∙ by Yanzhi Wang, et al. ∙ 3 ∙ share

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KCAT: A Knowledge-Constraint Typing Annotation Tool

Fine-grained Entity Typing is a tough task which suffers from noise samp...
06/13/2019 ∙ by Sheng Lin, et al. ∙ 2 ∙ share

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Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM

Weight quantization is one of the most important techniques of Deep Neur...
05/02/2019 ∙ by Sheng Lin, et al. ∙ 4 ∙ share

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ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning

The state-of-art DNN structures involve high computation and great deman...
04/30/2019 ∙ by Xiaolong Ma, et al. ∙ 0 ∙ share

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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Weight pruning and weight quantization are two important categories of D...
03/23/2019 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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Learning Topics using Semantic Locality

The topic modeling discovers the latent topic probability of the given t...
04/11/2018 ∙ by Ziyi Zhao, et al. ∙ 0 ∙ share

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FFT-Based Deep Learning Deployment in Embedded Systems

Deep learning has delivered its powerfulness in many application domains...
12/13/2017 ∙ by Sheng Lin, et al. ∙ 0 ∙ share

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Sheng Lin

Featured Co-authors

Fei Wu

Yanzhi Wang

Jian Tang

Xiang Ren

Sijia Liu

Yuan Xie

Xiang Chen

Bo Chen

Xue Lin

Tao Zhang

Yifan Gong

ESMFL: Efficient and Secure Models for Federated Learning

RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation

DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks

Deep Compressed Pneumonia Detection for Low-Power Embedded Devices

Learning Dynamic Context Augmentation for Global Entity Linking

An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM

Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation

Non-structured DNN Weight Pruning Considered Harmful

KCAT: A Knowledge-Constraint Typing Annotation Tool

Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM

ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Learning Topics using Semantic Locality

FFT-Based Deep Learning Deployment in Embedded Systems