42 citations · 96 across the 10 of their papers we have counts for
10 papers
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…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
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…
Optimizing Mixture of Experts using Dynamic Recompilations
Ferdinand Kossmann, Zhihao Jia, Alex Aiken
The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs). However, current…
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…
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs
Weiyang Wang, Moein Khazraee, Zhizhen Zhong +5
We propose TopoOpt, a novel direct-connect fabric for deep neural network (DNN) training workloads. TopoOpt co-optimizes the distributed training process across three dimensions: c…
Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities
Jessica Pointing, Oded Padon, Zhihao Jia +4
Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quan…
TOD: GPU-accelerated Outlier Detection via Tensor Operations
Yue Zhao, George H. Chen, Zhihao Jia
Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In th…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…