12 citations · 30 across the 9 of their papers we have counts for
9 papers
ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs
Guyue Huang, Yang Bai, Liu Liu +4
Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a syner…
Faith: An Efficient Framework for Transformer Verification on GPUs
Boyuan Feng, Tianqi Tang, Yuke Wang +5
Transformer verification draws increasing attention in machine learning research and industry. It formally verifies the robustness of transformers against adversarial attacks such…
AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs
Anbang Wu, Hezi Zhang, Gushu Li +3
Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum devices. However, the non-local quantum communication between…
Dynamic N:M Fine-grained Structured Sparse Attention Mechanism
Zhaodong Chen, Yuying Quan, Zheng Qu +3
Transformers are becoming the mainstream solutions for various tasks like NLP and Computer vision. Despite their success, the high complexity of the attention mechanism hinders the…
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Anbang Wu, Gushu Li, Yuke Wang +3
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
Mapping Surface Code to Superconducting Quantum Processors
Anbang Wu, Gushu Li, Hezi Zhang +3
In this paper, we formally describe the three challenges of mapping surface code on superconducting devices, and present a comprehensive synthesis framework to overcome these chall…
QECV: Quantum Error Correction Verification
Anbang Wu, Gushu Li, Hezi Zhang +3
Quantum Error Correction (QEC) is essential for fault-tolerant quantum copmutation, and its implementation is a very sophisticated process involving both quantum and classical hard…
Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
Anbang Wu, Gushu Li, Yufei Ding +1
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
Transformer Acceleration with Dynamic Sparse Attention
Liu Liu, Zheng Qu, Zhaodong Chen +2
Transformers are the mainstream of NLP applications and are becoming increasingly popular in other domains such as Computer Vision. Despite the improvements in model quality, the e…