14 citations · 16 across the 6 of their papers we have counts for
6 papers
Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances
Jiangfei Duan, Ziang Song, Xupeng Miao +5
Deep neural networks (DNNs) are becoming progressively large and costly to train. This paper aims to reduce DNN training costs by leveraging preemptible instances on modern clouds,…
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Zhengxin Zhang, Dan Zhao, Xupeng Miao +4
Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficien…
Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks
Zhihao Zhang, Zhuoming Chen, Heyang Huang +1
As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1)…
OLLIE: Derivation-based Tensor Program Optimizer
Liyan Zheng, Haojie Wang, Jidong Zhai +7
Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra exp…
Quartz: Superoptimization of Quantum Circuits (Extended Version)
Mingkuan Xu, Zikun Li, Oded Padon +8
Existing quantum compilers optimize quantum circuits by applying circuit transformations designed by experts. This approach requires significant manual effort to design and impleme…
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
John Thorpe, Pengzhan Zhao, Jonathan Eyolfson +5
DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organiza…