Hi! I am currently a Ph.D. student at Northeastern University, advised by Prof. Xue (Shelley) Lin and Prof. Yanzhi Wang, starting from Fall 2020. My research interests include efficient deep learning (pruning, quantization, and knowledge distillation), algorithm-hardware co-design, FPGAs, and electronic design automation.

Previously, I received my B.E. degree in microelectronic science and engineering from Fudan University, in 2020. During my undergrad, I was fortunate to work with Prof. Jianli Chen.

Experiences

• ByteDance, CA, USA, 2024.05-2024.08
  Research Scientist Intern in Seed Foundation Machine Learning System Team
  Mixture-of-Experts large language model pruning and expert-wise knowledge distillation

• Futurewei Technologies, CA, USA, 2023.05-2023.09
  Research Scientist Intern in Strategy Technology Team
  Efficient neural image compression model

Publications

[C17] MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
Yanyue Xie, Zhi Zhang, Ding Zhou, Cong Xie, Ziang Song, Xin Liu, Yanzhi Wang, Xue Lin, and An Xu
Under Review

[C16] LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference
Yanyue Xie, Zhengang Li, Dana Diaconu, Suranga Handagala, Miriam Leeser, and Xue Lin
ASPDAC 2025

[C15] HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
Lei Lu, Yanyue Xie, Wei Jiang, Wei Wang, Xue Lin, and Yanzhi Wang
ACMMM 2024

[C14] SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs
Geng Yang, Yanyue Xie, Zhong Jia Xue, Sung-En Chang, Yanyu Li, Peiyan Dong, Jie Lei, Weiying Xie, Yanzhi Wang, Xue Lin, and Zhenman Fang
FPL 2024

[C13] Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers
Zhengang Li, Alec Lu, Yanyue Xie, Zhenglun Kong, Mengshu Sun, Hao Tang, Zhong Jia Xue, Peiyan Dong, Caiwen Ding, Yanzhi Wang, Xue Lin, and Zhenman Fang
ICS 2024

[C12] SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits
Yanyue Xie*, Peiyan Dong*, Geng Yuan, Zhengang Li, Masoud Zabihi, Chao Wu, Sung-En Chang, Xufeng Zhang, Xue Lin, Caiwen Ding, Nobuyuki Yoshikawa, Olivia Chen, and Yanzhi Wang
DATE 2024

[C11] SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
Zhengang Li, Geng Yuan, Tomoharu Yamauchi, Masoud Zabihi, Yanyue Xie, Peiyan Dong, Xulong Tang, Nobuyuki Yoshikawa, Devesh Tiwari, Yanzhi Wang, and Olivia Chen
MICRO 2023

[C10] Machine Learning Across Network-Connected FPGAs
Dana Diaconu, Yanyue Xie, Mehmet Gungor, Suranga Handagala, Xue Lin, and Miriam Leeser
HPEC 2023

[C9] Algorithm-Software-Hardware Co-Design for Deep Learning Acceleration
Zhengang Li*, Yanyue Xie*, Peiyan Dong*, Olivia Chen, and Yanzhi Wang
DAC 2023

[C8] ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for 8-Bit DNN Training
Sung-En Chang, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li, Yanyue Xie, Minghai Qin, Xue Lin, Zhenman Fang, and Yanzhi Wang
DATE 2023

[C7] Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, Yanyue Xie, Peiyan Dong, Xin Meng, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, and Yanzhi Wang
AAAI 2023

[C6] HeatViT: Hardware-efficient adaptive token pruning for vision transformers
Peiyan Dong, Mengshu Sun, Alec Lu, Yanyue Xie, Kenneth Liu, Zhenglun Kong, Xin Meng, Zhengang Li, Xue Lin, Zhenman Fang, and Yanzhi Wang
HPCA 2023

[C5] You Already Have It: A Generator-Free Low-Precision DNN Training Framework using Stochastic Rounding
Geng Yuan, Sung-En Chang, Qing Jin, Alec Lu, Yanyu Li, Yushu Wu, Zhenglun Kong, Yanyue Xie, Peiyan Dong, Minghai Qin, Xiaolong Ma, Xulong Tang, Zhenman Fang, and Yanzhi Wang
ECCV 2022

[C4] Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization
Zhengang Li, Mengshu Sun, Alec Lu, Haoyu Ma, Geng Yuan, Yanyue Xie, Hao Tang, Yanyu Li, Miriam Leeser, Zhangyang Wang, Xue Lin, and Zhenman Fang
FPL 2022

[C3] TAAS: A Timing-Aware Analytical Strategy for AQFP-Capable Placement Automation
Peiyan Dong*, Yanyue Xie*, Hongjia Li*, Mengshu Sun, Olivia Chen, Nobuyuki Yoshikawa, and Yanzhi Wang
DAC 2022

[C2] Timing-Driven Placement for FPGAs with Heterogeneous Architectures and Clock Constraints
Zhifeng Lin, Yanyue Xie, Gang Qian, Jianli Chen, Sifei Wang, Jun Yu, and Yao-Wen Chang
DATE 2021

[C1] Late Breaking Results: An Analytical Timing-Driven Placer for Heterogeneous FPGAs
Zhifeng Lin, Yanyue Xie, Gang Qian, Sifei Wang, Jun Yu, and Jianli Chen
DAC 2020

[J3] HotaQ: Hardware Oriented Token Adaptive Quantization for Large Language Models
Xuan Shen, Zhaoyang Han, Lei Lu, Zhenglun Kong, Peiyan Dong, Zhengang Li, Yanyue Xie, Chao Wu, Miriam Leeser, Pu Zhao, Xue Lin, and Yanzhi Wang
TCAD 2024

[J2] Mixed-Cell-Height Placement with Complex Minimum-Implant-Area Constraints
Jianli Chen, Zhifeng Lin, Yanyue Xie, Wenxing Zhu, and Yao-Wen Chang
TCAD 2021

[J1] An Incremental Placement Flow for Advanced FPGAs with Timing Awareness
Zhifeng Lin, Yanyue Xie, Peng Zou, Sifei Wang, Jun Yu, and Jianli Chen
TCAD 2021

[P1] A Life-Cycle Energy and Inventory Analysis of Adiabatic Quantum-Flux-Parametron Circuits
Masoud Zabihi, Yanyue Xie, Zhengang Li, Peiyan Dong, Geng Yuan, Olivia Chen, Massoud Pedram, and Yanzhi Wang
arXiv preprint

Awards

DAC Young Fellow, Design Automation Conference, 2021
First Prize Scholarship, Fudan University, 2020