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Jin Liu - IEEE Xplore Author Profile

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Author details

Jin Liu

Publications

1

Citations

172

Publications by Year

20162016

Co-Authors:

Quntao AnZhuang PengLizhi SunLi Sun

Jin Liu

Affiliation

Department of Electrical Engineering

Harbin Institute of Technology

Harbin, China

Publication Topics

Back Electromotive Force,
Coupling Coefficient,
Current Control,
DC Link,
Dc-link Voltage,
Dead Time,
Dual Inverter,
Electric Vehicles,
Electromotive Force,
Factor Xa,
Induction Motor,
Insulated Gate Bipolar Transistor

Biography

Jin Liu received the B.S. degrees in physics and electrical engineering from the Harbin Institute of Technology, Harbin, China, in 2015, where he is currently working toward the M.S. degree in electrical engineering.,His research interests include electric machines and drives.(Based on document published on 22 January 2016).

Publications

1

Citations

172

Publications by Year

20162016

Co-Authors:

Quntao An
Zhuang Peng
Lizhi Sun
Li Sun

Author's Published Works

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Showing 1-25 of 29 results

Conferences (26)

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Bin Ren(29)
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College of William and Mary(3)
College of William & Mary(2)
Argonne National Laboratory, IL, USA(2)
2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)(2)
IEEE Transactions on Parallel and Distributed Systems(2)
2016 International Conference on Parallel Architecture and Compilation Techniques (PACT)(1)
2017 IEEE 13th International Conference on e-Science (e-Science)(1)
2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT)(1)
IEEE(29)
Media(1)
Nashville, TN, USA(3)
San Francisco, CA, USA(3)
Abu Dhabi, United Arab Emirates(1)
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Deep Neural Network(12)
Mobile Devices(11)
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Code Generation(6)
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Reducing Unfairness in Distributed Community Detection

Hao Zhang;Malith Jayaweera;Bin Ren;Yanzhi Wang;Sucheta Soundarajan

2024 IEEE International Conference on Data Mining (ICDM)

Year: 2024 | Conference Paper |

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Big graph data mining and processing have emerged as a crucial area of study. Distributed graph frameworks are commonly employed to process such big graph data in various applications. These frameworks have proven to be highly effective in improving both the accuracy and efficiency of processing large-scale graph data, but little attention has been paid to the algorithmic fairness of such methods....Show More

Reducing Unfairness in Distributed Community Detection

Hao Zhang;Malith Jayaweera;Bin Ren;Yanzhi Wang;Sucheta Soundarajan

2024 IEEE International Conference on Data Mining (ICDM)

Year: 2024 | Conference Paper |

DEFCON: Deformable Convolutions Leveraging Interval Search and GPU Texture Hardware

Malith Jayaweera;Yanyu Li;Yanzhi Wang;Bin Ren;David Kaeli

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2024 | Conference Paper |

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Deformable convolutions can improve detection accuracy in Convolution Neural Networks (CNNs) by leveraging flexible spatial sampling in augmenting kernels with learnable offsets. However, the resulting irregular memory access patterns and additional pixel lookup overhead introduced by deformable layers pose inherent challenges when executed on high-throughput devices such as GPUs. To address these...Show More

DEFCON: Deformable Convolutions Leveraging Interval Search and GPU Texture Hardware

Malith Jayaweera;Yanyu Li;Yanzhi Wang;Bin Ren;David Kaeli

2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2024 | Conference Paper |

DACO: Pursuing Ultra-low Power Consumption via DNN-Adaptive CPU-GPU CO-optimization on Mobile Devices

Yushu Wu;Chao Wu;Geng Yuan;Yanyu Li;Weichao Guo;Jing Rao;Xipeng Shen;Bin Ren;Yanzhi Wang

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)

Year: 2024 | Conference Paper |

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As Deep Neural Networks (DNNs) become popular in mobile systems, their high computational and memory demands make them major power consumers, especially in limited-budget scenarios. In this paper, we propose DACO, a DNN-Adaptive CPU-GPU CO-optimization technique, to reduce the power consumption of DNNs. First, a resource-oriented classifier is proposed to quantify the computation/memory intensity ...Show More

DACO: Pursuing Ultra-low Power Consumption via DNN-Adaptive CPU-GPU CO-optimization on Mobile Devices

Yushu Wu;Chao Wu;Geng Yuan;Yanyu Li;Weichao Guo;Jing Rao;Xipeng Shen;Bin Ren;Yanzhi Wang

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)

Year: 2024 | Conference Paper |

Unfairness in Distributed Graph Frameworks

Hao Zhang;Malith Jayaweera;Bin Ren;Yanzhi Wang;Sucheta Soundarajan

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

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In the era of big data, distributed graph processing frameworks have become important in processing large-scale graph datasets. Such distributed frameworks exhibit major advantages with respect to scalability, and provide various ways to speed up sequential graph algorithms. However, the literature lacks an analysis on the fairness properties of such distributed algorithms. In this work, we analyz...Show More

Unfairness in Distributed Graph Frameworks

Hao Zhang;Malith Jayaweera;Bin Ren;Yanzhi Wang;Sucheta Soundarajan

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge

Changdi Yang;Pu Zhao;Yanyu Li;Wei Niu;Jiexiong Guan;Hao Tang;Minghai Qin;Bin Ren;Xue Lin;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (9)

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With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications. Vision Transformers (ViTs) have shown considerably stronger results for many vision tasks. However, ViTs with the fullattention mechanism usually consume a large number of computational resources, leading to difficulties for real- ...Show More

Pruning Parameterization with Bi-level Optimization for Efficient Semantic Segmentation on the Edge

Changdi Yang;Pu Zhao;Yanyu Li;Wei Niu;Jiexiong Guan;Hao Tang;Minghai Qin;Bin Ren;Xue Lin;Yanzhi Wang

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Efficient Quantum Circuit Cutting by Neglecting Basis Elements

Daniel T. Chen;Ethan H. Hansen;Xinpeng Li;Vinooth Kulkarni;Vipin Chaudhary;Bin Ren;Qiang Guan;Sanmukh Kuppannagari;Ji Liu;Shuai Xu

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through scales exponentially. We propose a simpl...Show More

Efficient Quantum Circuit Cutting by Neglecting Basis Elements

Daniel T. Chen;Ethan H. Hansen;Xinpeng Li;Vinooth Kulkarni;Vipin Chaudhary;Bin Ren;Qiang Guan;Sanmukh Kuppannagari;Ji Liu;Shuai Xu

2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

Year: 2023 | Conference Paper |

Towards Socially Acceptable Food Type Recognition

Junjie Wang;Jiexiong Guan;Y. Alicia Hong;Hong Xue;Shuangquan Wang;Zhenming Liu;Bin Ren;Gang Zhou

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

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Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user's food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of t...Show More

Towards Socially Acceptable Food Type Recognition

Junjie Wang;Jiexiong Guan;Y. Alicia Hong;Hong Xue;Shuangquan Wang;Zhenming Liu;Bin Ren;Gang Zhou

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

GCD2: A Globally Optimizing Compiler for Mapping DNNs to Mobile DSPs

Wei Niu;Jiexiong Guan;Xipeng Shen;Yanzhi Wang;Gagan Agrawal;Bin Ren

2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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More specialized chips are exploiting available high transistor density to expose parallelism at a large scale with more intricate instruction sets. This paper reports on a compilation system GCD2, developed to support complex Deep Neural Network (DNN) workloads on mobile DSP chips. We observe several challenges in fully exploiting this architecture, related to SIMD width, more complex SIMD/vector...Show More

GCD2: A Globally Optimizing Compiler for Mapping DNNs to Mobile DSPs

Wei Niu;Jiexiong Guan;Xipeng Shen;Yanzhi Wang;Gagan Agrawal;Bin Ren

2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Year: 2022 | Conference Paper |

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions

Qihan Wang;Bin Ren;Jie Chen;Robert G. Edwards

2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2022 | Conference Paper |

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Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at sp...Show More

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions

Qihan Wang;Bin Ren;Jie Chen;Robert G. Edwards

2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Year: 2022 | Conference Paper |

Brief Industry Paper: Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

Hsin-Hsuan Sung;Yuanchao Xu;Jiexiong Guan;Wei Niu;Bin Ren;Yanzhi Wang;Shaoshan Liu;Xipeng Shen

2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2022 | Conference Paper |

Cited by: Papers (4)

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In the past few years we have developed hardware computing systems for commercial autonomous vehicles, but inevitably the high development cost and long turn-around time have been major roadblocks for commercial deployment. Hence we also explored the potential of software optimization. This paper, for the first-time, shows that it is feasible to enable full leve1-4 autonomous driving workloads on ...Show More

Brief Industry Paper: Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card

Hsin-Hsuan Sung;Yuanchao Xu;Jiexiong Guan;Wei Niu;Bin Ren;Yanzhi Wang;Shaoshan Liu;Xipeng Shen

2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2022 | Conference Paper |

BLCR: Towards Real-time DNN Execution with Block-based Reweighted Pruning

Xiaolong Ma;Geng Yuan;Zhengang Li;Yifan Gong;Tianyun Zhang;Wei Niu;Zheng Zhan;Pu Zhao;Ning Liu;Jian Tang;Xue Lin;Bin Ren;Yanzhi Wang

2022 23rd International Symposium on Quality Electronic Design (ISQED)

Year: 2022 | Conference Paper |

Cited by: Papers (3)

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Accelerating DNN execution on resource-limited computing platforms has been a long-standing problem. Prior works utilize ℓ1-based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel computing architectures. However, both of the pruning schemes and pruning methods lack universality, which leads to degraded performance and limited a...Show More

BLCR: Towards Real-time DNN Execution with Block-based Reweighted Pruning

Xiaolong Ma;Geng Yuan;Zhengang Li;Yifan Gong;Tianyun Zhang;Wei Niu;Zheng Zhan;Pu Zhao;Ning Liu;Jian Tang;Xue Lin;Bin Ren;Yanzhi Wang

2022 23rd International Symposium on Quality Electronic Design (ISQED)

Year: 2022 | Conference Paper |

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Mert Hidayetoğlu;Tekin Biçer;Simon Garcia de Gonzalo;Bin Ren;Doğa Gürsoy;Rajkumar Kettimuthu;Ian T. Foster;Wen-Mei W. Hwu

IEEE Transactions on Parallel and Distributed Systems

Year: 2022 | Volume: 33, Issue: 9 | Journal Article |

Cited by: Papers (4)

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This work extends our previous research entitled “MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization” that was originally published at SC19 conference (Hidayetoğlu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high r...Show More

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Mert Hidayetoğlu;Tekin Biçer;Simon Garcia de Gonzalo;Bin Ren;Doğa Gürsoy;Rajkumar Kettimuthu;Ian T. Foster;Wen-Mei W. Hwu

IEEE Transactions on Parallel and Distributed Systems

Year: 2022 | Volume: 33, Issue: 9 | Journal Article |

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity

Wei Niu;Zhengang Li;Xiaolong Ma;Peiyan Dong;Gang Zhou;Xuehai Qian;Xue Lin;Yanzhi Wang;Bin Ren

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Cited by: Papers (10)

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It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as “resource-constrained” when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilita...Show More

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity

Wei Niu;Zhengang Li;Xiaolong Ma;Peiyan Dong;Gang Zhou;Xuehai Qian;Xue Lin;Yanzhi Wang;Bin Ren

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search

Zheng Zhan;Yifan Gong;Pu Zhao;Geng Yuan;Wei Niu;Yushu Wu;Tianyun Zhang;Malith Jayaweera;David Kaeli;Bin Ren;Xue Lin;Yanzhi Wang

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

Cited by: Papers (31)

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Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices. To overcome the challenge and facilitate the real-time deploymen...Show More

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search

Zheng Zhan;Yifan Gong;Pu Zhao;Geng Yuan;Wei Niu;Yushu Wu;Tianyun Zhang;Malith Jayaweera;David Kaeli;Bin Ren;Xue Lin;Yanzhi Wang

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2021 | Conference Paper |

HEALS: A Parallel eALS Recommendation System on CPU/GPU Heterogeneous Platforms

Qihan Wang;Wei Niu;Li Chen;Ruoming Jin;Bin Ren

2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Alternating Least Square (ALS) is a classic algorithm to solve matrix factorization widely used in recommendation systems. Existing efforts focus on parallelizing ALS on multi-/many-core platforms to handle large datasets. Recently, an optimized ALS variant called eALS was proposed, and it yields significantly lower time complexity and higher recommending accuracy than ALS. However, it is challeng...Show More

HEALS: A Parallel eALS Recommendation System on CPU/GPU Heterogeneous Platforms

Qihan Wang;Wei Niu;Li Chen;Ruoming Jin;Bin Ren

2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)

Year: 2021 | Conference Paper |

A High Performance Sparse Tensor Algebra Compiler in MLIR

Ruiqin Tian;Luanzheng Guo;Jiajia Li;Bin Ren;Gokcen Kestor

2021 IEEE/ACM 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC)

Year: 2021 | Conference Paper |

Cited by: Papers (22)

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Sparse tensor algebra is widely used in many applications, including scientific computing, machine learning, and data analytics. The performance of sparse tensor algebra kernels strongly depends on the intrinsic characteristics of the input tensors, hence many storage formats are designed for tensors to achieve optimal performance for particular applications/architectures, which makes it challengi...Show More

A High Performance Sparse Tensor Algebra Compiler in MLIR

Ruiqin Tian;Luanzheng Guo;Jiajia Li;Bin Ren;Gokcen Kestor

2021 IEEE/ACM 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC)

Year: 2021 | Conference Paper |

Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles

Pu Zhao;Geng Yuan;Yuxuan Cai;Wei Niu;Qi Liu;Wujie Wen;Bin Ren;Yanzhi Wang;Xue Lin

2021 58th ACM/IEEE Design Automation Conference (DAC)

Year: 2021 | Conference Paper |

Cited by: Papers (8)

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Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a compiler-aware neural pruning search framework to achieve high-speed inference on autonomous vehicles for 2D and 3D object detection. The framework automati...Show More

Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles

Pu Zhao;Geng Yuan;Yuxuan Cai;Wei Niu;Qi Liu;Wujie Wen;Bin Ren;Yanzhi Wang;Xue Lin

2021 58th ACM/IEEE Design Automation Conference (DAC)

Year: 2021 | Conference Paper |

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

Zhengang Li;Geng Yuan;Wei Niu;Pu Zhao;Yanyu Li;Yuxuan Cai;Xuan Shen;Zheng Zhan;Zhenglun Kong;Qing Jin;Zhiyu Chen;Sijia Liu;Kaiyuan Yang;Bin Ren;Yanzhi Wang;Xue Lin

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (15)

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With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently, and do not fully consider compiler-level optimizations which is a must-do for mobile a...Show More

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

Zhengang Li;Geng Yuan;Wei Niu;Pu Zhao;Yanyu Li;Yuxuan Cai;Xuan Shen;Zheng Zhan;Zhenglun Kong;Qing Jin;Zhiyu Chen;Sijia Liu;Kaiyuan Yang;Bin Ren;Yanzhi Wang;Xue Lin

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

Geng Yuan;Peiyan Dong;Mengshu Sun;Wei Niu;Zhengang Li;Yuxuan Cai;Jun Liu;Weiwen Jiang;Xue Lin;Bin Ren;Xulong Tang;Yanzhi Wang

2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Although various optimization approaches have been proven to be effective in many DNNs on edge devices, most state-of-the-art work focuses on ad-hoc optimizations, and there lacks a thorough st...Show More

Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

Geng Yuan;Peiyan Dong;Mengshu Sun;Wei Niu;Zhengang Li;Yuxuan Cai;Jun Liu;Weiwen Jiang;Xue Lin;Bin Ren;Xulong Tang;Yanzhi Wang

2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2021 | Conference Paper |

Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search

Pu Zhao;Wei Niu;Geng Yuan;Yuxuan Cai;Hsin-Hsuan Sung;Shaoshan Liu;Sijia Liu;Xipeng Shen;Bin Ren;Yanzhi Wang;Xue Lin

2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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In autonomous driving, 3D object detection is es-sential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support realtime 3D object detection on edge-computing devices in selfdriving cars with limited computation and memory resources. To facilitate this, we propose a compiler-aware pr...Show More

Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search

Pu Zhao;Wei Niu;Geng Yuan;Yuxuan Cai;Hsin-Hsuan Sung;Shaoshan Liu;Sijia Liu;Xipeng Shen;Bin Ren;Yanzhi Wang;Xue Lin

2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)

Year: 2021 | Conference Paper |

Real-Time Mobile Acceleration of DNNs: From Computer Vision to Medical Applications

Hongjia Li;Geng Yuan;Wei Niu;Yuxuan Cai;Mengshu Sun;Zhengang Li;Bin Ren;Xue Lin;Yanzhi Wang

2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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With the growth of mobile vision applications, there is a growing need to break through the current performance limitation of mobile platforms, especially for computationally intensive applications, such as object detection, action recognition, and medical diagnosis. To achieve this goal, we present our unified real-time mobile DNN inference acceleration framework, seamlessly integrating hardware-...Show More

Real-Time Mobile Acceleration of DNNs: From Computer Vision to Medical Applications

Hongjia Li;Geng Yuan;Wei Niu;Yuxuan Cai;Mengshu Sun;Zhengang Li;Bin Ren;Xue Lin;Yanzhi Wang

2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC)

Year: 2021 | Conference Paper |

Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes

Mert Hidayetoğlu;Tekin Bicer;Simon Garcia de Gonzalo;Bin Ren;Vincent De Andrade;Doga Gursoy;Raj Kettimuthu;Ian T. Foster;Wen-mei W. Hwu

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2020 | Conference Paper |

Cited by: Papers (14)

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X-ray computed tomography is a commonly used technique for noninvasive imaging at synchrotron facilities. Iterative tomographic reconstruction algorithms are often preferred for recovering high quality 3D volumetric images from 2D X-ray images, however, their use has been limited to small/medium datasets due to their computational requirements. In this paper, we propose a high-performance iterativ...Show More

Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes

Mert Hidayetoğlu;Tekin Bicer;Simon Garcia de Gonzalo;Bin Ren;Vincent De Andrade;Doga Gursoy;Raj Kettimuthu;Ian T. Foster;Wen-mei W. Hwu

SC20: International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2020 | Conference Paper |

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

Peiyan Dong;Siyue Wang;Wei Niu;Chengming Zhang;Sheng Lin;Zhengang Li;Yifan Gong;Bin Ren;Xue Lin;Dingwen Tao

2020 57th ACM/IEEE Design Automation Conference (DAC)

Year: 2020 | Conference Paper |

Cited by: Papers (31)

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Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become promising and important on mobile devices such as smart phones. However, previous RNN compression techniques either suffer from hardware performance overhead due to irregularity or significant accuracy loss due to the preserved regularity for hardware friendliness. In this work, we propose RTMobile that leverag...Show More

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

Peiyan Dong;Siyue Wang;Wei Niu;Chengming Zhang;Sheng Lin;Zhengang Li;Yifan Gong;Bin Ren;Xue Lin;Dingwen Tao

2020 57th ACM/IEEE Design Automation Conference (DAC)

Year: 2020 | Conference Paper |

NVGraph: Enforcing Crash Consistency of Evolving Network Analytics in NVMM Systems

Soklong Lim;Tyler Coy;Zaixin Lu;Bin Ren;Xuechen Zhang

IEEE Transactions on Parallel and Distributed Systems

Year: 2020 | Volume: 31, Issue: 6 | Journal Article |

Cited by: Papers (5)

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Many complex networks can be modeled as evolving graphs. Existing in-memory graph data structures are designed for DRAM. Thus, they cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent for NVMM. (2) NVMM writes and reads and may incur higher latency than DRAM. In this ar...Show More

NVGraph: Enforcing Crash Consistency of Evolving Network Analytics in NVMM Systems

Soklong Lim;Tyler Coy;Zaixin Lu;Bin Ren;Xuechen Zhang

IEEE Transactions on Parallel and Distributed Systems

Year: 2020 | Volume: 31, Issue: 6 | Journal Article |

MemXCT: Memory-Centric X-Ray CT Reconstruction with Massive Parallelization

Mert Hidayetoğlu;Tekin Biçer;Simon Garcia de Gonzalo;Bin Ren;Doğa Gürsoy;Rajkumar Kettimuthu;Ian T. Foster;Wen-mei W. Hwu

SC19: International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2019 | Conference Paper |

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X-ray computed tomography (XCT) is used regularly at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be terabyte-scale. Advanced i...Show More

MemXCT: Memory-Centric X-Ray CT Reconstruction with Massive Parallelization

Mert Hidayetoğlu;Tekin Biçer;Simon Garcia de Gonzalo;Bin Ren;Doğa Gürsoy;Rajkumar Kettimuthu;Ian T. Foster;Wen-mei W. Hwu

SC19: International Conference for High Performance Computing, Networking, Storage and Analysis

Year: 2019 | Conference Paper |

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