29 citations · 32 across the 5 of their papers we have counts for
5 papers
Closing the Train-Test Gap in World Models for Gradient-Based Planning
Arjun Parthasarathy, Nimit Kalra, Rohun Agrawal +4
World models paired with model predictive control (MPC) can be trained offline on large-scale datasets of expert trajectories and enable generalization to a wide range of planning…
Partitioning and Deployment of Deep Neural Networks on Edge Clusters
Arjun Parthasarathy, Bhaskar Krishnamachari
Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becomin…
SEIFER: Scalable Edge Inference for Deep Neural Networks
Arjun Parthasarathy, Bhaskar Krishnamachari
Edge inference is becoming ever prevalent through its applications from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common,…
Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput
Arjun Parthasarathy, Bhaskar Krishnamachari
Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becomin…
DEFER: Distributed Edge Inference for Deep Neural Networks
Arjun Parthasarathy, Bhaskar Krishnamachari
Modern machine learning tools such as deep neural networks (DNNs) are playing a revolutionary role in many fields such as natural language processing, computer vision, and the inte…