Search Results for author: Tong Geng

Found 24 papers, 9 papers with code

Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids

no code implementations15 Mar 2024 Yanfei Li, Juejing Liu, Xiaodong Zhao, Wenjun Liu, Tong Geng, Ang Li, Xin Zhang

Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data.

Binary Classification Multi-Task Learning

Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs

no code implementations8 Nov 2023 Hongwu Peng, Caiwen Ding, Tong Geng, Sutanay Choudhury, Kevin Barker, Ang Li

The relentless advancement of artificial intelligence (AI) and machine learning (ML) applications necessitates the development of specialized hardware accelerators capable of handling the increasing complexity and computational demands.

ClusterFormer: Clustering As A Universal Visual Learner

1 code implementation22 Sep 2023 James C. Liang, Yiming Cui, Qifan Wang, Tong Geng, Wenguan Wang, Dongfang Liu

This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER.

Clustering Image Classification +7

Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks

1 code implementation22 Aug 2023 Xi Xie, Hongwu Peng, Amit Hasan, Shaoyi Huang, Jiahui Zhao, Haowen Fang, Wei zhang, Tong Geng, Omer Khan, Caiwen Ding

Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.

Computational Efficiency

TransFlow: Transformer as Flow Learner

no code implementations CVPR 2023 Yawen Lu, Qifan Wang, Siqi Ma, Tong Geng, Yingjie Victor Chen, Huaijin Chen, Dongfang Liu

Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement.

Motion Estimation object-detection +4

Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation

no code implementations20 Mar 2023 Xiaodong Zhao, YiXuan Luo, Juejing Liu, Wenjun Liu, Kevin M. Rosso, Xiaofeng Guo, Tong Geng, Ang Li, Xin Zhang

This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.

Towards Real-Time Temporal Graph Learning

1 code implementation8 Oct 2022 Deniz Gurevin, Mohsin Shan, Tong Geng, Weiwen Jiang, Caiwen Ding, Omer Khan

Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time.

graph construction Graph Learning +3

MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms

1 code implementation14 Sep 2022 yuke wang, Boyuan Feng, Zheng Wang, Tong Geng, Kevin Barker, Ang Li, Yufei Ding

For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.

Layout Design Management

Towards Sparsification of Graph Neural Networks

1 code implementation11 Sep 2022 Hongwu Peng, Deniz Gurevin, Shaoyi Huang, Tong Geng, Weiwen Jiang, Omer Khan, Caiwen Ding

In this paper, we utilize two state-of-the-art model compression methods (1) train and prune and (2) sparse training for the sparsification of weight layers in GNNs.

Image Classification Link Prediction +4

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

no code implementations28 Jun 2022 Chengming Zhang, Tong Geng, Anqi Guo, Jiannan Tian, Martin Herbordt, Ang Li, Dingwen Tao

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs.

BIG-bench Machine Learning

GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm

1 code implementation5 Jun 2022 Yanfei Li, Tong Geng, Samuel Stein, Ang Li, Huimin Yu

To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs.


I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization

no code implementations7 Mar 2022 Tong Geng, Chunshu Wu, Yongan Zhang, Cheng Tan, Chenhao Xie, Haoran You, Martin C. Herbordt, Yingyan Lin, Ang Li

In this paper we propose a novel hardware accelerator for GCN inference, called I-GCN, that significantly improves data locality and reduces unnecessary computation.

G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency

no code implementations18 Sep 2021 Yongan Zhang, Haoran You, Yonggan Fu, Tong Geng, Ang Li, Yingyan Lin

While end-to-end jointly optimizing GNNs and their accelerators is promising in boosting GNNs' inference efficiency and expediting the design process, it is still underexplored due to the vast and distinct design spaces of GNNs and their accelerators.

Binary Complex Neural Network Acceleration on FPGA

no code implementations10 Aug 2021 Hongwu Peng, Shanglin Zhou, Scott Weitze, Jiaxin Li, Sahidul Islam, Tong Geng, Ang Li, Wei zhang, Minghu Song, Mimi Xie, Hang Liu, Caiwen Ding

Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts.

Decision Making

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

1 code implementation23 Jun 2021 Boyuan Feng, yuke wang, Tong Geng, Ang Li, Yufei Ding

Over the years, accelerating neural networks with quantization has been widely studied.


BCNN: Binary Complex Neural Network

no code implementations28 Mar 2021 Yanfei Li, Tong Geng, Ang Li, Huimin Yu

Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network -- a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs.

Comparison Lift: Bandit-based Experimentation System for Online Advertising

no code implementations16 Sep 2020 Tong Geng, Xiliang Lin, Harikesh S. Nair, Jun Hao, Bin Xiang, Shurui Fan

Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser.

Experimental Design

Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments

no code implementations4 Jul 2019 Tong Geng, Xiliang Lin, Harikesh S. Nair

The product is currently deployed on the advertising platform of JD. com, an eCommerce company and a publisher of digital ads in China.

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

no code implementations4 Jan 2019 Tong Geng, Tianqi Wang, Ang Li, Xi Jin, Martin Herbordt

Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size.

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