no code implementations • 1 Mar 2024 • Adiba Orzikulova, Diana A. Vasile, Fahim Kawsar, Chulhong Min
As wearable devices become increasingly miniaturized and powerful, a new opportunity arises for instant and dynamic device-to-device collaboration and human-to-device interaction.
no code implementations • 25 Jan 2024 • Chulhong Min, Juheon Yi, Utku Gunay Acer, Fahim Kawsar
Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis.
no code implementations • 11 Dec 2023 • Taesik Gong, Si Young Jang, Utku Günay Acer, Fahim Kawsar, Chulhong Min
The advent of tiny AI accelerators opens opportunities for deep neural network deployment at the extreme edge, offering reduced latency, lower power cost, and improved privacy in on-device ML inference.
no code implementations • 1 Feb 2022 • Yash Jain, Chi Ian Tang, Chulhong Min, Fahim Kawsar, Akhil Mathur
In this paper, we extend this line of research and present a novel technique called Collaborative Self-Supervised Learning (ColloSSL) which leverages unlabeled data collected from multiple devices worn by a user to learn high-quality features of the data.
no code implementations • 8 Sep 2021 • Chulhong Min, Akhil Mathur, Utku Gunay Acer, Alessandro Montanari, Fahim Kawsar
We present SensiX++ - a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e. g., a camera, a microphone, or IoT sensors.
no code implementations • 4 Dec 2020 • Chulhong Min, Akhil Mathur, Alessandro Montanari, Utku Gunay Acer, Fahim Kawsar
The emergence of multiple sensory devices on or near a human body is uncovering new dynamics of extreme edge computing.