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09/17/2023
Performance of Graph Neural Networks for Point Cloud Applications
Graph Neural Networks (GNNs) have gained significant momentum recently d...
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09/17/2023
Dynasor: A Dynamic Memory Layout for Accelerating Sparse MTTKRP for Tensor Decomposition on Multi-core CPU
Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the most...
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09/13/2023
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) has achieved significant succe...
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08/04/2023
Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration
Graph Neural Networks (GNNs) have revolutionized many Machine Learning (...
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07/14/2023
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynami...
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05/11/2023
Graph Neural Network for Accurate and Low-complexity SAR ATR
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the...
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04/19/2023
HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN
Predicting the throughput of WLAN deployments is a classic problem that ...
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03/22/2023
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation
Graph Neural Network (GNN) inference is used in many real-world applicat...
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03/02/2023
HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform
As the size of real-world graphs increases, training Graph Neural Networ...
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03/01/2023
HyScale-GNN: A Scalable Hybrid GNN Training System on Single-Node Heterogeneous Architecture
Graph Neural Networks (GNNs) have shown success in many real-world appli...
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02/02/2023
GraphAGILE: An FPGA-based Overlay Accelerator for Low-latency GNN Inference
This paper presents GraphAGILE, a domain-specific FPGA-based overlay acc...
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01/04/2023
Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA
Synthetic aperture radar (SAR) automatic target recognition (ATR) is the...
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09/02/2022
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning
In Multi-Agent Reinforcement Learning, communication is critical to enco...
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08/23/2022
Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform
Monte Carlo Tree Search (MCTS) methods have achieved great success in ma...
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08/22/2022
Performance Modeling Sparse MTTKRP Using Optical Static Random Access Memory on FPGA
Electrical static random memory (E-SRAM) is the current standard for int...
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07/17/2022
Towards Programmable Memory Controller for Tensor Decomposition
Tensor decomposition has become an essential tool in many data science a...
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06/17/2022
Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform
Mini-batch inference of Graph Neural Networks (GNNs) is a key problem in...
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03/28/2022
Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware
Runtime-reconfigurable software coupled with reconfigurable hardware is ...
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03/10/2022
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA
Temporal Graph Neural Networks (TGNNs) are powerful models to capture te...
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01/19/2022
Decoupling the Depth and Scope of Graph Neural Networks
State-of-the-art Graph Neural Networks (GNNs) have limited scalability w...
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12/22/2021
HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
Graph Neural Networks (GNNs) have shown great success in many applicatio...
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10/24/2021
Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery
Wing and Tip decomposition construct a hierarchy of butterfly-dense edge...
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09/18/2021
Reconfigurable Low-latency Memory System for Sparse Matricized Tensor Times Khatri-Rao Product on FPGA
Tensor decomposition has become an essential tool in many applications i...
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09/09/2021
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings
Temporal Knowledge Graphs store events in the form of subjects, relation...
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08/21/2021
Programmable FPGA-based Memory Controller
Even with generational improvements in DRAM technology, memory access la...
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05/10/2021
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Graph Neural Networks (GNNs) are proven to be powerful models to generat...
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12/02/2020
Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
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12/02/2020
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference
Most of the existing works on FPGA acceleration of Convolutional Neural ...
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10/17/2020
RECEIPT: REfine CoarsE-grained IndePendent Tasks for Parallel Tip decomposition of Bipartite Graphs
Tip decomposition is a crucial kernel for mining dense subgraphs in bipa...
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10/05/2020
Accurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to genera...
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12/31/2019
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art...
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10/16/2019
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
To accelerate inference of Convolutional Neural Networks (CNNs), various...
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07/10/2019
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning rep...
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06/29/2019
Planting Trees for scalable and efficient Canonical Hub Labeling
Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in ...
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10/28/2018
Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerfu...
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06/21/2018
GPOP: A cache- and work-efficient framework for Graph Processing Over Partitions
The past decade has seen development of many shared-memory graph process...
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09/21/2017