328 citations · 1.2k across the 57 of their papers we have counts for
11 papers · 1 filter
Reuse Kernels or Activations? A Flexible Dataflow for Low-latency Spectral CNN Acceleration
Yue Niu, Rajgopal Kannan, Ajitesh Srivastava +1
Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a `kernel explosion' pro…
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Samuel Wiggins, Yuan Meng, Rajgopal Kannan +1
Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing adva…
Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration
Paul Chen, Pavan Manjunath, Sasindu Wijeratne +2
Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by…
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Hongkuan Zhou, Da Zheng, Xiang Song +2
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications.…
Graph Neural Network for Accurate and Low-complexity SAR ATR
Bingyi Zhang, Sasindu Wijeratne, Rajgopal Kannan +2
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the key technique for remote sensing image recognition. The state-of-the-art works exploit the deep convolution…
HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN
Hongkuan Zhou, Rajgopal Kannan, Ananthram Swami +1
Predicting the throughput of WLAN deployments is a classic problem that occurs in the design of robust and high performance WLAN systems. However, due to the increasingly complex c…
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation
Bingyi Zhang, Viktor Prasanna
Graph Neural Network (GNN) inference is used in many real-world applications. Data sparsity in GNN inference, including sparsity in the input graph and the GNN model, offer opportu…
HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
As the size of real-world graphs increases, training Graph Neural Networks (GNNs) has become time-consuming and requires acceleration. While previous works have demonstrated the po…
HyScale-GNN: A Scalable Hybrid GNN Training System on Single-Node Heterogeneous Architecture
Yi-Chien Lin, Viktor Prasanna
Graph Neural Networks (GNNs) have shown success in many real-world applications that involve graph-structured data. Most of the existing single-node GNN training systems are capabl…
GraphAGILE: An FPGA-based Overlay Accelerator for Low-latency GNN Inference
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
This paper presents GraphAGILE, a domain-specific FPGA-based overlay accelerator for graph neural network (GNN) inference. GraphAGILE consists of (1) \emph{a novel unified architec…
Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA
Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna +1
Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key technique for remote-sensing image recognition. The state-of-the-art convolutional neural networks (CNN…