328 citations · 1.2k across the 57 of their papers we have counts for
10 papers · 1 filter
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…