3 citations · 3 across the 6 of their papers we have counts for
6 papers
NeuroPrefetcher: Storage-Aware Sparse LLM Inference via Delta Prefetching
Nobel Dhar, Md Romyull Islam, Xuechen Zhang +4
Deploying large language models on edge devices is increasingly limited by a widening gap between model size and available memory. Existing approaches such as quantization, smaller…
FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment
Nazmus Shakib Shadin, Aaron Cummings, Xinyue Zhang +1
Federated learning (FL) is a decentralized approach that enables collaborative model training without exposing raw data. Instead of transferring sensitive data, it allows devices t…
Characterizing and Understanding Energy Footprint and Efficiency of Small Language Model on Edges
Md Romyull Islam, Bobin Deng, Nobel Dhar +4
Cloud-based large language models (LLMs) and their variants have significantly influenced real-world applications. Deploying smaller models (i.e., small language models (SLMs)) on…
Activation Sparsity Opportunities for Compressing General Large Language Models
Nobel Dhar, Bobin Deng, Md Romyull Islam +3
Deploying local AI models, such as Large Language Models (LLMs), to edge devices can substantially enhance devices' independent capabilities, alleviate the server's burden, and low…
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
Manh V. Nguyen, Liang Zhao, Bobin Deng +1
Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a ris…
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Manh V. Nguyen, Liang Zhao, Bobin Deng +3
Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Arti…