Rajgopal Kannan

research

∙ 09/17/2023

Performance of Graph Neural Networks for Point Cloud Applications

Graph Neural Networks (GNNs) have gained significant momentum recently d...

0 Dhruv Parikh, et al. ∙

research

∙ 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...

0 Sasindu Wijeratne, et al. ∙

research

∙ 09/13/2023

Characterizing Speed Performance of Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has achieved significant succe...

0 Samuel Wiggins, et al. ∙

research

∙ 05/11/2023

Graph Neural Network for Accurate and Low-complexity SAR ATR

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the...

0 Bingyi Zhang, et al. ∙

research

∙ 04/19/2023

HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN

Predicting the throughput of WLAN deployments is a classic problem that ...

0 Hongkuan Zhou, et al. ∙

research

∙ 01/04/2023

Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA

Synthetic aperture radar (SAR) automatic target recognition (ATR) is the...

0 Bingyi Zhang, et al. ∙

research

∙ 12/10/2022

Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics

Graph processing applications are severely bottlenecked by memory system...

0 Pengmiao Zhang, et al. ∙

research

∙ 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...

0 Yuan Meng, et al. ∙

research

∙ 07/17/2022

Towards Programmable Memory Controller for Tensor Decomposition

Tensor decomposition has become an essential tool in many data science a...

0 Sasindu Wijeratne, et al. ∙

research

∙ 05/29/2022

TransforMAP: Transformer for Memory Access Prediction

Data Prefetching is a technique that can hide memory latency by fetching...

0 Pengmiao Zhang, et al. ∙

research

∙ 05/01/2022

Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching

Machine learning algorithms have shown potential to improve prefetching ...

0 Pengmiao Zhang, et al. ∙

research

∙ 03/28/2022

Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware

Runtime-reconfigurable software coupled with reconfigurable hardware is ...

0 Hongkuan Zhou, et al. ∙

research

∙ 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...

0 Hongkuan Zhou, et al. ∙

research

∙ 01/19/2022

Decoupling the Depth and Scope of Graph Neural Networks

State-of-the-art Graph Neural Networks (GNNs) have limited scalability w...

0 Hanqing Zeng, et al. ∙

research

∙ 10/24/2021

Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery

Wing and Tip decomposition construct a hierarchy of butterfly-dense edge...

0 Kartik Lakhotia, et al. ∙

research

∙ 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...

0 Sasindu Wijeratne, et al. ∙

research

∙ 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...

0 Hongkuan Zhou, et al. ∙

research

∙ 08/21/2021

Programmable FPGA-based Memory Controller

Even with generational improvements in DRAM technology, memory access la...

0 Sasindu Wijeratne, et al. ∙

research

∙ 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...

0 Hongkuan Zhou, et al. ∙

research

∙ 12/02/2020

Deep Graph Neural Networks with Shallow Subgraph Samplers

While Graph Neural Networks (GNNs) are powerful models for learning repr...

0 Hanqing Zeng, et al. ∙

research

∙ 12/02/2020

DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference

Most of the existing works on FPGA acceleration of Convolutional Neural ...

0 Yuan Meng, et al. ∙

research

∙ 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...

0 Kartik Lakhotia, et al. ∙

research

∙ 10/05/2020

Accurate, Efficient and Scalable Training of Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep learning models to genera...

0 Hanqing Zeng, et al. ∙

research

∙ 03/17/2020

Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction

Writing high-performance code requires significant expertise in the prog...

0 Ajitesh Srivastava, et al. ∙

research

∙ 10/16/2019

SPEC2: SPECtral SParsE CNN Accelerator on FPGAs

To accelerate inference of Convolutional Neural Networks (CNNs), various...

0 Yue Niu, et al. ∙

research

∙ 07/10/2019

GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning rep...

0 Hanqing Zeng, et al. ∙

research

∙ 06/29/2019

Planting Trees for scalable and efficient Canonical Hub Labeling

Point-to-Point Shortest Distance (PPSD) query is a crucial primitive in ...

0 Kartik Lakhotia, et al. ∙

research

∙ 10/28/2018

Accurate, Efficient and Scalable Graph Embedding

The Graph Convolutional Network (GCN) model and its variants are powerfu...

0 Hanqing Zeng, et al. ∙

research

∙ 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...

0 Kartik Lakhotia, et al. ∙

research

∙ 09/21/2017

Accelerating PageRank using Partition-Centric Processing

PageRank is a fundamental link analysis algorithm and a key representati...

0 Kartik Lakhotia, et al. ∙

Rajgopal Kannan

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