7 citations · 9 across the 5 of their papers we have counts for
5 papers
HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity
Abdullah Al Asif, Patrick Diem, Juan Pablo Muñoz +3
Distributed Low-Communication (DiLoCo) training reduces communication overhead by allowing workers to perform multiple local optimization steps before sending pseudo-gradients to a…
SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
Abdullah Al Asif, Sixing Yu, Juan Pablo Munoz +2
SplitFed Learning (SFL) combines federated learning and split learning to enable collaborative training across distributed edge devices; however, it faces significant challenges in…
PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation
Branden Butler, Sixing Yu, Arya Mazaheri +1
Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from C…
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
Sixing Yu, Arya Mazaheri, Ali Jannesari
Model compression is an essential technique for deploying deep neural networks (DNNs) on power and memory-constrained resources. However, existing model-compression methods often r…
Auto Graph Encoder-Decoder for Neural Network Pruning
Sixing Yu, Arya Mazaheri, Ali Jannesari
Model compression aims to deploy deep neural networks (DNN) on mobile devices with limited computing and storage resources. However, most of the existing model compression methods…