305 citations · 669 across the 22 of their papers we have counts for
22 papers
BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs
Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen +2
Recent studies have shown that Binary Graph Neural Networks (GNNs) are promising for saving computations of GNNs through binarized tensors. Prior work, however, mainly focused on a…
MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems
Jieyang Chen, Chenhao Xie, Jesun S Firoz +5
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra k…
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
Tong Geng, Chunshu Wu, Yongan Zhang +6
Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of…
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
Yongan Zhang, Haoran You, Yonggan Fu +3
Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for graph-based learning tasks. However, it still remains prohibitively challenging to inference GNN…
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search
Hongwu Peng, Shiyang Chen, Zhepeng Wang +9
Molecular similarity search has been widely used in drug discovery to identify structurally similar compounds from large molecular databases rapidly. With the increasing size of ch…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression
Chengming Zhang, Sian Jin, Tong Geng +3
As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounde…
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Boyuan Feng, Yuke Wang, Tong Geng +2
Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activati…
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
Samuel A. Stein, Betis Baheri, Daniel Chen +5
In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore's Law era, however, the limit of semiconductor fabric…
Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures
Chenhao Xie, Jieyang Chen, Jesun S Firoz +5
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload…
A Hybrid System for Learning Classical Data in Quantum States
Samuel A. Stein, Ryan L'Abbate, Wenrui Mu +6
Deep neural network powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, tr…
ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing
Cheng Tan, Chenhao Xie, Tong Geng +4
The next generation HPC and data centers are likely to be reconfigurable and data-centric due to the trend of hardware specialization and the emergence of data-driven applications.…
QuGAN: A Quantum State Fidelity based Generative Adversarial Network
Samuel A. Stein, Betis Baheri, Daniel Chen +5
Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain. As a r…
Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs
Ang Li, Simon Su
Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-pu…
CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks
Runbin Shi, Peiyan Dong, Tong Geng +6
Recurrent neural networks (RNNs) have been widely adopted in temporal sequence analysis, where realtime performance is often in demand. However, RNNs suffer from heavy computationa…
QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation
Ang Li, Samuel Stein, Sriram Krishnamoorthy +1
The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of…
A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs
Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu +3
Tensor computations present significant performance challenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analy…
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Tong Geng, Ang Li, Runbin Shi +8
Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are bett…
Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect
Ang Li, Shuaiwen Leon Song, Jieyang Chen +4
High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and…
PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite
Jiajia Li, Yuchen Ma, Xiaolong Wu +2
Tensor methods have gained increasingly attention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mini…
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Linnan Wang, Jinmian Ye, Yiyang Zhao +5
Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) pr…
Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards
Ang Li, Radu Serban, Dan Negrut
We discuss an approach for solving sparse or dense banded linear systems on a Graphics Processing Unit (GPU) card. The matrix ${\bf A} \in {\mathbb{R}}^…