328 citations · 1.2k across the 57 of their papers we have counts for
58 papers
PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR Images
Sasindu Wijeratne, Bingyi Zhang, Rajgopal Kannan +2
Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Ne…
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…
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning
Diyi Hu, Chi Zhang, Viktor Prasanna +1
In Multi-Agent Reinforcement Learning, communication is critical to encourage cooperation among agents. Communication in realistic wireless networks can be highly unreliable due to…
Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform
Yuan Meng, Rajgopal Kannan, Viktor Prasanna
Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck…
Performance Modeling Sparse MTTKRP Using Optical Static Random Access Memory on FPGA
Sasindu Wijeratne, Akhilesh Jaiswal, Ajey P. Jacob +2
Electrical static random memory (E-SRAM) is the current standard for internal static memory in Field Programmable Gate Array (FPGA). Despite the dramatic improvement in E-SRAM tech…
Towards Programmable Memory Controller for Tensor Decomposition
Sasindu Wijeratne, Ta-Yang Wang, Rajgopal Kannan +1
Tensor decomposition has become an essential tool in many data science applications. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the pivotal kernel in tensor deco…
Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform
Bingyi Zhang, Hanqing Zeng, Viktor Prasanna
Mini-batch inference of Graph Neural Networks (GNNs) is a key problem in many real-world applications. Recently, a GNN design principle of model depth-receptive field decoupling ha…
TransforMAP: Transformer for Memory Access Prediction
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program. Prefetching relies on accurate memory access prediction, to which ta…
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori +2
Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling o…
Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware
Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan +1
Runtime-reconfigurable software coupled with reconfigurable hardware is highly desirable as a means towards maximizing runtime efficiency without compromising programmability. Comp…
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA
Hongkuan Zhou, Bingyi Zhang, Rajgopal Kannan +2
Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings ou…
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…
HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform
Yi-Chien Lin, Bingyi Zhang, Viktor Prasanna
Graph Neural Networks (GNNs) have shown great success in many applications such as recommendation systems, molecular property prediction, traffic prediction, etc. Recently, CPU-FPG…
Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery
Kartik Lakhotia, Rajgopal Kannan, Viktor Prasanna
Wing and Tip decomposition construct a hierarchy of butterfly-dense edge and vertex induced bipartite subgraphs, respectively. They have applications in several domains including e…