2 citations · 2 across the 5 of their papers we have counts for
5 papers
Beyond HBM-on-GPU: Thermal Design Envelope for 3D Volumetric DRAM-on-GPU Integration
Yukai Chen, Melina Lofrano, Khakim Akhunov +12
The scaling of GPUs for AI and HPC workloads is increasingly constrained by the capacity, bandwidth, and thermal limits of both 2.5D HBM-GPU and direct-stacked 3D HBM-on-GPU integr…
Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
Jonas Svedas, Nathan Laubeuf, Ryan Harvey +6
Predicting the performance of large-scale distributed machine learning (ML) workloads across multiple accelerator architectures remains a central challenge in ML system design. Exi…
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf +6
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth i…
A Study of Optimizations for Fine-tuning Large Language Models
Arjun Singh, Nikhil Pandey, Anup Shirgaonkar +2
Fine-tuning large language models is a popular choice among users trying to adapt them for specific applications. However, fine-tuning these models is a demanding task because the…
SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise
Abhay Kumar Yadav, Arjun Singh
In this paper, we introduce a novel fine-tuning technique for language models, which involves incorporating symmetric noise into the embedding process. This method aims to enhance…