Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Champati, Jaya"'
Autor:
Díaz-Aranda, Sergio, Ramírez, Juan Marcos, Daga, Mohit, Champati, Jaya Prakash, Aguilar, José, Lillo, Rosa Elvira, Anta, Antonio Fernández
Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number
Externí odkaz:
http://arxiv.org/abs/2407.10640
Autor:
Behera, Adarsh Prasad, Daubaris, Paulius, Bravo, Iñaki, Gallego, José, Morabito, Roberto, Widmer, Joerg, Champati, Jaya Prakash Varma
On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedded in resource-limited devices, use cases are limited to simple infere
Externí odkaz:
http://arxiv.org/abs/2407.11061
The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective meth
Externí odkaz:
http://arxiv.org/abs/2406.09424
Autor:
Al-Atat, Ghina, Fresa, Andrea, Behera, Adarsh Prasad, Moothedath, Vishnu Narayanan, Gross, James, Champati, Jaya Prakash
Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in
Externí odkaz:
http://arxiv.org/abs/2304.11763
We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a microcontroller unit, embedded with a small-size ML model (S-ML) for a generic classification application and an Edge Server (ES) that hosts a large-size ML model (L-ML).
Externí odkaz:
http://arxiv.org/abs/2304.00891
This paper contributes tail bounds of the age-of-information of a general class of parallel systems and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile networks, multi-technology wireless access, or mu
Externí odkaz:
http://arxiv.org/abs/2303.14035
Autor:
Muñoz, Manuel O. J. Olguín, Moothedath, Vishnu N., Champati, Jaya Prakash, Klatzky, Roberta, Satyanarayanan, Mahadev, Gross, James
Wearable Cognitive Assistance (WCA) applications present a challenge to benchmark and characterize due to their human-in-the-loop nature. Employing user testing to optimize system parameters is generally not feasible, given the scope of the problem a
Externí odkaz:
http://arxiv.org/abs/2212.06100
Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone of such IoT-based systems, however, their resource limitations hamper the continuous ex
Externí odkaz:
http://arxiv.org/abs/2211.07130
Autor:
Fresa, Andrea, Champati, Jaya Prakash
With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Lea
Externí odkaz:
http://arxiv.org/abs/2112.11413
We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency resul
Externí odkaz:
http://arxiv.org/abs/2109.09474