About Me

I am a final-year Ph.D. Student at Northeastern University, Boston, advised by Prof. Yanzhi Wang.

I’m on the academic job market this year. Please reach out for any opportunities!

My research area is the intersection of Software-Hardware Co-design, Efficient AI Systems, Hardware Architecture, and Efficient Emerging Devices:

• Hardware and Software Co-design for DNN Architecture
• Inference/Energy-Efficient Artificial Intelligence Systems
• Efficient Emerging Deep Learning Systems (Superconducting devices, Quantum Circuits)

Summary: There are 37 publications on top conferences/journals ranging from: (I) EDA, solid-state circuit, and system conferences such as DAC, ICCAD, DATE, ISSCC, ASP-DAC, RTAS, MLSys. (II) Architecture and computer system conferences such as MICRO, HPCA, ICS. (III) Machine learning algorithm conferences such as NeurIPS, ICML, CVPR, AAAI, ECCV, IJCAI, AAAI. (IV) Journal publications including TCAD, Advanced Intelligent Systems, TCASI, TECS, TPAMI.

• Among these, there are 18 first/co-first author publications.
• I also received an EECS Rising Star Award in the EECS Rising Stars 2023.

For more details, please see my CV.

News

• December 2023, one paper gets accepted at AAAI 2024.
• November 2023, I gave a talk on “Efficient AI on the Next-Generation Computing” in the Symposium on Frontiers in Innovative Technology held by the University of Michigan and Shanghai Jiao Tong University.
• November 2023, one design automation tool for Superconducting circuits, AQFP, gets accepted at DATE 2023.
• September 2023, two papers get accepted at Neurips 2023.
• To be honored, I was selected as a Rising Star in EECS 2023.
• August 2023, one paper gets accepted at ICCAD 2023.
• July 2023, I gave a talk on “Software-Hardware Co-Design: Towards Ultimate Efficiency in Deep Learning Acceleration” at the 4TH ROAD4NN WORKSHOP, DAC 2023, San Francisco, CA, USA.
• July 2023, I gave a talk on “Algorithm-Software-Hardware Co-Design for AI Acceleration” at the Session - TINYML: BRING DEEP LEARNING MODELS TO TINY DEVICES, DAC 2023, San Francisco, CA, USA

Selected Publications (Full list)

• HPCA 2023 HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

  Peiyan Dong, Mengshu Sun, Alec Lu, Yanyue Xie, Kenneth Liu, Xue Lin, Zhenman Fang, Yanzhi Wang

• DAC 2022 TAAS: A Timing-Aware Analytical Strategy for AQFP-Capable Placement Automation

  Peiyan Dong, Yanyue Xie, Hongjia Li, Olivia Chen, Mengshu Sun, Nobuyuki Yoshikawa, Yanzhi Wang

• DAC 2020 Rtmobile: Beyond real-time mobile acceleration of rnns for speech recognition

  Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Bin Ren, Xue Lin, Dingwen Tao

• TCAD 2023 HetaViT: Hardware-Efficient and Token-Aware Joint Compression with Pruning and Quantization for Vision Transformers

  Peiyan Dong, Mengshu Sun, Yanyue Xie, Xue Lin, Zhenman Fang, Yanzhi Wang

• ASP-DAC 2024 FF-Medical: Fast and Fair Medical AI on the Edge through Hardware-oriented Search for Hybrid Vision Models

  Peiyan Dong, Changdi Yang, Yi Sheng, Yanyu Li, Lei Yang, Xue Lin, Yanzhi Wang

• NeurIPS 2023 HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception

  Peiyan Dong, Zhenglun Kong, Xin Meng, Geng Yuan, Fei Sun, Hao Tang, Yanzhi Wang

• NeurIPS 2023 PackQViT: Faster Sub-8-bit Vision Transformers via Full and Packed Quantization on the Mobile

  Peiyan Dong, Lei Lu, Chao Wu, Cheng Lyu, Geng Yuan, Hao Tang, Yanzhi Wang

• ICML 2023 SpeedDETR: Speed-aware Transformers for End-to-end Object Detection

  Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang, Yanzhi Wang, Chih-Hsien Chou

• ICS 2020 CSB-RNN: a faster-than-realtime RNN acceleration framework with compressed structured blocks

  Runbin Shi*, Peiyan Dong*, Tong Geng, Yuhao Ding, Xiaolong Ma, Hayden K-H So, Martin Herbordt, Ang Li, Yanzhi Wang

• TECS 2021 Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

  Peiyan Dong, Guan Geng, Mengshu Sun, Wei Niu, Zhengang Li, Yuxuan Cai, Yanyu Li et al

• AAAI 2024 Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge

  Xuan Shen*, Peiyan Dong*, Lei Lu, Zhenglun Kong, Zhengang Li, Ming Lin, Chao Wu, Yanzhi Wang

• ECCV 2022 SPViT: Enabling Faster Vision Transformers via Soft Token Pruning

  Zhenglun Kong*, Peiyan Dong*, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang

• MICRO 2023 SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices

  Zhengang Li, Geng Yuan, Tomoharu Yamauchi, Zabihi Masoud, Yanyue Xie, Peiyan Dong, Xulong Tang, Nobuyuki Yoshikawa, Devesh Tiwari, Yanzhi Wang, Olivia Chen

• ICCAD 2023 Fast and Fair Medical AI on the Edge through Neural Architecture Search for Hybrid Vision Models

  Peiyan Dong, Changdi Yang, Yi Sheng, Zhenglun Kong, Yanyu Li, Lei Yang, Xue Lin

• DAC 2023 Algorithm-Software-Hardware Co-Design for Deep Learning Acceleration

  Zhengang Li*, Peiyan Dong*, Yanyue Xie, Olivia Chen, Yanzhi Wang

• DATE 2023 SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits

  Peiyan Dong, Yanyue Xie, Geng Yuan, Zhengang Li, Masoud Zabihi, Chao Wu, Sung-En Chang, Xufeng Zhang, Xue Lin, Caiwen Ding, Nobuyuki Yoshikawa, Olivia Chen, Yanzhi Wang

• DAC 2023 Fast Fair Medical Applications? Hybrid Vision Models Achieve the Fairness on the Edge: Late Breaking Results

  Changdi Yang*, Peiyan Dong*, Yi Sheng, Zhenglun Kong, Yanyu Li, Pinrui Yu, Lei Yang, Xue Lin

• DAC 2023 WIP SuperFlow: An RTL-to-GDS Design Automation Flow for AQFP Superconducting Devices

  Yanyue Xie*, Peiyan Dong*, Geng Yuan, Zhengang Li, Xufeng Zhang, Olivia Chen, Nobuyuki Yoshikawa, Yanzhi Wang

• ICCAD 2022 Quantum Neural Network Compression

  Zhirui Hu, Peiyan Dong, Zhepeng Wang, Youzuo Lin, Yanzhi Wang, Weiwen Jiang

• RTAS 2021 Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

  Geng Yuan*, Peiyan Dong*, Mengshu Sun, Wei Niu, Zhengang Li, Yuxuan Cai, Jun Liu, Weiwen Jiang, Xue Lin, Bin Ren, Xulong Tang, Yanzhi Wang

• ASP-DAC 2021 Puncturing the memory wall: Joint optimization of network compression with approximate memory for ASR application

  Qin Li*, Peiyan Dong*, Zijie Yu, Changlu Liu, Fei Qiao, Yanzhi Wang, Huazhong Yang

• ISSCC 2021 SRP A 22.3 nJ/Frame low-Memory beyond-real-Time keyword Spotting Chip with Configurable Feature Extraction and Distributed Perceptual Computation

  Qin Li, Changlu Liu, Peiyan Dong, Yanming Zhang, Tong Li, Minda Yang, Fei Qiao, Yanzhi Wang, Li Luo, Huazhong Yang

• Advanced Intelligent Systems 2022 (Impact Factor 7.4) Floating Gate Transistor-Based Accurate Digital In-Memory Computing for Deep Neural Networks

  Runze Han, Peng Huang, Yachen Xiang, Hong Hu, Sheng Lin, Peiyan Dong, Wensheng Shen, Yanzhi Wang, Xiaoyan Liu, Jinfeng Kang

• TPAMI 2022 (Impact Factor 23.6) GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

  Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren

• TCASI 2021 (Impact Factor 5.1) NS-FDN: Near-Sensor Processing Architecture of Feature Configurable Distributed Network for Beyond-Real-Time Always-on Keyword Spotting

  Qin Li, Changlu Liu, Peiyan Dong, Yanming Zhang, Tong Li, Sheng Lin, Minda Yang, Fei Qiao, Yanzhi Wang, Li Luo, Huazhong Yang

• AAAI 2023 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, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, Yanzhi Wang

• IJCAI 2023 Data Level Lottery Ticket Hypothesis for Vision Transformers

  Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Geng Yuan, Xin Meng, Hao Tang, Xiaolong Ma, Yanzhi Wang

• ECCV 2022 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, Yanzhi Wang

• MLSys 2021 A Compiler-aware Framework of Network Pruning Search Achieving Beyond Real-Time Mobile Acceleration

  Yanyu Li, Geng Yuan, Zhengang Li, Wei Niu, Pu Zhao, Peiyan Dong, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Bin Ren, Yanzhi Wang, Xue Lin

• AAAI 2022 DARB: A Density-Adaptive Regular-Block Pruning for Deep Neural Networks

  Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin, Peiyan Dong, Yen-kuang Chen, Yuan Xie, Yanzhi Wang

Patents

• US Patent Computer-implemented methods and systems for compressing recurrent neural network (rnn) models and accelerating rnn execution in mobile devices to achieve real-time inference

  Yanzhi Wang, Peiyan Dong, Zhengang Li, Bin Ren, Wei Niu

Invited Talks

• Symposium on Frontiers in Innovative Technology, University of Michigan/Shanghai Jiao Tong University, 2023 Shanghai, China.

  TALK: Efficient AI on the Next-Generation Computing

• 4TH ROAD4NN WORKSHOP, DAC 2023 San Francisco, CA, USA

  TALK: Software-Hardware Co-Design: Towards Ultimate Efficiency in Deep Learning Acceleration

• TINYML: BRING DEEP LEARNING MODELS TO TINY DEVICES, DAC 2023 San Francisco, CA, USA

  TALK: Algorithm-Software-Hardware Co-Design for AI Acceleration

Honors

• EECS Rising Star Award, The EECS Rising Stars Workshop, 2023
• Northeastern University Dissertation Completion Fellowships, 2023
• Oral Paper Award, The 37th Annual AAAI Conference on Artificial Intelligence, 2022
• Oral Paper Award, The 37th Annual IJCAI Conference on Artificial Intelligence, 2022
• Spotlight Paper Award, The IEEE / CVF Computer Vision and Pattern Recognition Conference Workshop, 2022
• M Award, The Mathematical Contest in Modeling (MCM)/The Interdisciplinary Contest in Modeling (ICM), 2015
• 1st Award (National Level), China National College Students Math Modelling Competition 2015
• H Award, The Mathematical Contest in Modeling (MCM)/The Interdisciplinary Contest in Modeling (ICM), 2014
• 1st Award (Provincial Level), China National College Students Math Modelling Competition 2014
• 1st Award, BIT Math Modelling Competition of Beijing Institute of Technology, 2014
• 2nd Award, BIT Math Modelling Competition of Beijing Institute of Technology, 2013

Education

• Ph.D. Candidate in Computer Engineering, GPA: 4.0/4.0

• M.S. in Operation Research, GPA: 3.9/4.0

  01/2017 – 05/2024

  Northeastern University, Boston, MA, USA

• Team Organizer in Mathematical Modeling Training Center

  06/2016 – 12/2016

  Beijing Institute of Technology, Beijing, China

• Bachelor’s in Information Engineering GPA: 3.8/4.0

  06/2012 – 06/2016

  Beijing Institute of Technology, Beijing, China

Service

Invited Reviewer for ICML, NeurIPS, ICLR, EMNLP, JMLR, TCAS-I, TMLR, TCAD, AAAI, ECCV, ICCV, HPCA, DAC, ICCAD.