1 citations · 1 across the 10 of their papers we have counts for
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RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference
George Karfakis, Lime Yao, Binglu Chen +5
RAPID-LLM is a unified performance modeling framework for distributed large language model (LLM) training and inference on GPU clusters, without relying on deployment-specific trac…
D-com: Accelerating Iterative Processing to Enable Low-rank Decomposition of Activations
Faraz Tahmasebi, Michael Pelluer, Hyoukjun Kwon
The computation and memory costs of large language models kept increasing over last decade, which reached over the scale of 1T parameters. To address the challenges from the large…
Exploring the Dynamic Scheduling Space of Real-Time Generative AI Applications on Emerging Heterogeneous Systems
Rachid Karami, Rajeev Patwari, Hyoukjun Kwon +1
The integration of generative AI models, particularly large language models (LLMs), into real-time multi-model AI applications such as video conferencing and gaming is giving rise…
Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Saptarshi Mitra, Rachid Karami, Haocheng Xu +2
Emerging applications such as AR are driving demands for machine intelligence capable of processing continuous and/or long-context inputs on local devices. However, currently domin…