most citedRethinking Internet Communication Through LLMs: How Close Are We?

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2024

FusedInf: Efficient Swapping of DNN Models for On-Demand Serverless Inference Services on the Edge

Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis

Edge AI computing boxes are a new class of computing devices that are aimed to revolutionize the AI industry. These compact and robust hardware units bring the power of AI processi…

cs.LG2024

UnifiedNN: Efficient Neural Network Training on the Cloud

Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis

Nowadays, cloud-based services are widely favored over the traditional approach of locally training a Neural Network (NN) model. Oftentimes, a cloud service processes multiple requ…

cs.LG2024

Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud

Sifat Ut Taki, Spyridon Mastorakis

Training a proprietary Neural Network (NN) model with a proprietary dataset on the cloud comes at the risk of exposing the model architecture and the dataset to the cloud service p…

cs.AR2023

Gem5Pred: Predictive Approaches For Gem5 Simulation Time

Tian Yan, Xueyang Li, Sifat Ut Taki +1

Gem5, an open-source, flexible, and cost-effective simulator, is widely recognized and utilized in both academic and industry fields for hardware simulation. However, the typically…

cs.NI20231 cited

Rethinking Internet Communication Through LLMs: How Close Are We?

Sifat Ut Taki, Spyridon Mastorakis

In this paper, we rethink the way that communication among users over the Internet, one of the fundamental outcomes of the Internet evolution, takes place. Instead of users communi…