1 citations · 3 across the 7 of their papers we have counts for
8 papers
EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
Motahare Mounesan, Irfan Khan
Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogeneous sensor modalities, such…
Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction
Motahare Mounesan, Sourya Saha, Houchao Gan +2
Real-time multi-view 3D reconstruction is a mission-critical application for key edge-native use cases, such as fire rescue, where timely and accurate 3D scene modeling enables sit…
Variational Autoencoder-Based Black-Box Adversarial Attack on Collaborative DNN Inference
Shima Yousefi, Motahare Mounesan, Saptarshi Debroy
In recent years, Deep Neural Networks (DNNs) have become increasingly integral to IoT-based environments, enabling realtime visual computing. However, the limited computational cap…
Infer-EDGE: Dynamic DNN Inference Optimization in 'Just-in-time' Edge-AI Implementations
Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy
Balancing mutually diverging performance metrics, such as end-to-end latency, accuracy, and device energy consumption, is a challenging undertaking for deep neural network (DNN) in…
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy
Balancing mutually diverging performance metrics, such as, processing latency, outcome accuracy, and end device energy consumption is a challenging undertaking for deep learning mo…
VECA: Reliable and Confidential Resource Clustering for Volunteer Edge-Cloud Computing
Hemanth Sai Yeddulapalli, Mauro Lemus Alarcon, Upasana Roy +5
Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogene…