2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.DC2025
Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
Lang Xu, Quentin Anthony, Jacob Hatef +4
Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that dras…
cs.LG2024★ 1 cited
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference
Jinghan Yao, Quentin Anthony, Aamir Shafi +3
In large language models like the Generative Pre-trained Transformer, the Mixture of Experts paradigm has emerged as a powerful technique for enhancing model expressiveness and acc…
cs.PF2023★ 2 cited
Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version
Hyunho Ahn, Tian Chen, Nawras Alnaasan +5
Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native har…