178 citations · 1.1k across the 84 of their papers we have counts for
26 papers · 1 filter
The Urban Vision Hackathon Dataset and Models: Towards Image Annotations and Accurate Vision Models for Indian Traffic
Akash Sharma, Chinmay Mhatre, Sankalp Gawali +10
This report describes the UVH-26 dataset, the first public release by AIM@IISc of a large-scale dataset of annotated traffic-camera images from India. The dataset comprises 26,646…
AeroResQ: Edge-Accelerated UAV Framework for Scalable, Resilient and Collaborative Escape Route Planning in Wildfire Scenarios
Suman Raj, Radhika Mittal, Rajiv Mayani +5
Drone fleets equipped with onboard cameras, computer vision, and Deep Neural Network (DNN) models present a powerful paradigm for real-time spatio-temporal decision-making. In wild…
Towards AI Agents for Course Instruction in Higher Education: Early Experiences from the Field
Yogesh Simmhan, Varad Kulkarni
This article presents early findings from designing, deploying and evaluating an AI-based educational agent deployed as the primary instructor in a graduate-level Cloud Computing c…
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…
Fulcrum: Optimizing Concurrent DNN Training and Inferencing on Edge Accelerators
Prashanthi S. K., Saisamarth Taluri, Pranav Gupta +6
The proliferation of GPU accelerated edge devices like Nvidia Jetsons and the rise in privacy concerns are placing an emphasis on concurrent DNN training and inferencing on edge de…
Pagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators
Prashanthi S. K., Kunal Kumar Sahoo, Amartya Ranjan Saikia +4
Edge accelerators such as Nvidia Jetsons are becoming an integral part of the computing continuum, and are often used for DNN inferencing and training. Nvidia Jetson edge devices h…
Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models
Prashanthi S. K., Sai Anuroop Kesanapalli, Yogesh Simmhan
Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous vehicles and smart cities through low-latency inferencing on edge computing devices close to th…
AgentX: Towards Orchestrating Robust Agentic Workflow Patterns with FaaS-hosted MCP Services
Shiva Sai Krishna Anand Tokal, Vaibhav Jha, Anand Eswaran +2
Generative Artificial Intelligence (GenAI) has rapidly transformed various fields including code generation, text summarization, image generation and so on. Agentic AI is a recent…
Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
Roopkatha Banerjee, Sampath Koti, Gyanendra Singh +4
Real-time monitoring of power consumption in cities and micro-grids through the Internet of Things (IoT) can help forecast future demand and optimize grid operations. But moving al…
Characterizing FaaS Workflows on Public Clouds: The Good, the Bad and the Ugly
Varad Kulkarni, Nikhil Reddy, Tuhin Khare +3
Function-as-a-service (FaaS) is a popular serverless computing paradigm for developing event-driven functions that elastically scale on public clouds. FaaS workflows, such as AWS S…
Lightweight Targeted Estimation of Layout Noise in a Quantum Computer using Quality Indicator Circuits
Shikhar Srivastava, Ritajit Majumdar, Padmanabha Venkatagiri Seshadri +2
Minimizing noise is essential for reliably executing quantum circuits on the current generation of hardware. The mapping of an abstract quantum circuit to the physical layout of th…
AerialDB: A Federated Peer-to-Peer Spatio-temporal Edge Datastore for Drone Fleets
Shashwat Jaiswal, Suman Raj, Subhajit Sidhanta +1
Recent years have seen an unprecedented growth in research that leverages the newest computing paradigm of Internet of Drones, comprising a fleet of connected Unmanned Aerial Vehic…
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
Harsha Varun Marisetty, Manik Gupta, Yogesh Simmhan
With advancements in computing and communication technologies, the Internet of Things (IoT) has seen significant growth. IoT devices typically collect data from various sensors, su…
Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources
Roopkatha Banerjee, Prince Modi, Jinal Vyas +5
With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning(FL) has rapidly gained popularity as a privacy-preserving, distrib…
Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
Suman Raj, Swapnil Padhi, Ruchi Bhoot +2
Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logisti…
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…
Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
Roopkatha Banerjee, Tejus Chandrashekar, Ananth Eswar +1
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, optimizing both energy efficiency and model accuracy…
Understanding the Performance and Power of LLM Inferencing on Edge Accelerators
Mayank Arya, Yogesh Simmhan
Large Language Models (LLMs) have demonstrated exceptional benefits to a wide range of domains, for tasks as diverse as code generation and robot navigation. While LLMs are usually…
Ripple: Scalable Incremental GNN Inferencing on Large Streaming Graphs
Pranjal Naman, Yogesh Simmhan
Most real-world graphs are dynamic in nature, with continuous and rapid updates to the graph topology, and vertex and edge properties. Such frequent updates pose significant challe…
AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service
Suman Raj, Rajdeep Singh, Kautuk Astu +1
The increasing adoption of UAVs with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in fields such as precision agriculture, wildf…
NeoARCADE: Robust Calibration for Distance Estimation to Support Assistive Drones for the Visually Impaired
Suman Raj, Bhavani A Madhabhavi, Madhav Kumar +2
Autonomous navigation by drones using onboard sensors, combined with deep learning and computer vision algorithms, is impacting a number of domains. We examine the use of drones to…
Ocularone-Bench: Benchmarking DNN Models on GPUs to Assist the Visually Impaired
Suman Raj, Bhavani A Madhabhavi, Kautuk Astu +3
VIP navigation requires multiple DNN models for identification, posture analysis, and depth estimation to ensure safe mobility. Using a hazard vest as a unique identifier enhances…
A Blockchain-Enabled Framework for Storage and Retrieval of Social Data
Aishwarya Parab, Prakhar Pradhan, Yogesh Simmhan +1
The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trus…
Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device
Mumuksh Tayal, Yogesh Simmhan
Edge devices like Nvidia Jetson platforms now offer several on-board accelerators -- including GPU CUDA cores, Tensor Cores, and Deep Learning Accelerators (DLA) -- which can be co…