7 papers
Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs
Pranjal Naman, Yogesh Simmhan
Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core sett…
ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks
Pranjal Naman, Yogesh Simmhan
Graph Neural Network (GNN) inference on billion-scale graphs is critical for domains like fintech and recommendation systems. Full-graph inference on these large graphs can be chal…
Scaling Real-Time Traffic Analytics on Edge-Cloud Fabrics for City-Scale Camera Networks
Akash Sharma, Pranjal Naman, Roopkatha Banerjee +11
Real-time city-scale traffic analytics requires processing 100s-1000s of CCTV streams under strict latency, bandwidth, and compute limits. We present a scalable AI-driven Intellige…
RIPPLE++: An Incremental Framework for Efficient GNN Inference on Evolving Graphs
Pranjal Naman, Parv Agarwal, Hrishikesh Haritas +1
Real-world graphs are dynamic, with frequent updates to their structure and features due to evolving vertex and edge properties. These continual changes pose significant challenges…
OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks
Pranjal Naman, Yogesh Simmhan
Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. However, in mo…
Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
Pranjal Naman, Yogesh Simmhan
Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Lear…