research
∙
09/17/2023
Performance of Graph Neural Networks for Point Cloud Applications
Graph Neural Networks (GNNs) have gained significant momentum recently d...
share
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...
research
∙
09/13/2023
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) has achieved significant succe...
research
∙
05/11/2023
Graph Neural Network for Accurate and Low-complexity SAR ATR
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the...
research
∙
04/19/2023
HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN
Predicting the throughput of WLAN deployments is a classic problem that ...
research
∙
01/04/2023
Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA
Synthetic aperture radar (SAR) automatic target recognition (ATR) is the...
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...
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...
research
∙
07/17/2022
Towards Programmable Memory Controller for Tensor Decomposition
Tensor decomposition has become an essential tool in many data science a...
research
∙
05/29/2022
TransforMAP: Transformer for Memory Access Prediction
Data Prefetching is a technique that can hide memory latency by fetching...
research
∙
05/01/2022
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Machine learning algorithms have shown potential to improve prefetching ...
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 ...
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...
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...
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...
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...
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...
research
∙
08/21/2021
Programmable FPGA-based Memory Controller
Even with generational improvements in DRAM technology, memory access la...
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...
research
∙
12/02/2020
Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
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 ...
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...
research
∙
10/05/2020
Accurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to genera...
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...
research
∙
10/16/2019
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
To accelerate inference of Convolutional Neural Networks (CNNs), various...
research
∙
07/10/2019
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning rep...
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 ...
research
∙
10/28/2018
Accurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerfu...
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...
research
∙
09/21/2017
