I am a fourth-year PhD student in SeeLab at UCSD, advised by Prof. Tajana Rosing. My research interests include a) emerging processing-in-memory (PIM) architectures, b) near-storage computing system, c) hyperdimensional computing.

Before joining UCSD, I received B.E. and M.E. degrees from Southeast University, under supervision of Prof. Chuan Zhang and Prof. Yair Be’ery. I finished a four-month research intern in Intel Labs China where I developed the Flexible MIMO Processor for networks beyond 5G.

🔥 News

• 2024.12:  🎉 I will be joining EPFL as a postdoctoral researcher.
• 2024.09:  🎉 I have successfully completed my Ph.D. at UCSD! Check my thesis here
• 2024.07:  🎉 HyperGen work was accepted!
• 2024.07:  🎉 TriHD work was accepted!

📖 Educations

• 2020 - 2024, Ph.D. in Computer Science and Engineering, University of California, San Diego, La Jolla, USA.
• 2017 - 2020, M.E. in Information and Communication Engineering, Southeast University, Nanjing, China.
• 2013 - 2017, B.E. in Information Engineering, Southeast University, Nanjing, China.

Highlighted Projects

🧬 Bioinformatics: Genome and Mass Spectrometry Analysis

Bioinformatics 2024

HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors
Weihong Xu, Po-kai Hsu, Niema Moshiri, Shimeng Yu, and Tajana Rosing
Bioinformatics, 2024

• HyperGen is a memory-efficient tool for genomic sketching

JPR 2023

HyperSpec: Fast Mass Spectra Clustering in Hyperdimensional Space
Weihong Xu, Jaeyoung Kang, Wout Bittremieux, Niema Moshiri, and Tajana Rosing
Journal of Proteome Research, 2023

• HyperSpec is one of the fastest clustering tool for mass spectrometry data

• [TCAD 2023] RAPIDx: High-performance ReRAM Processing in-Memory Accelerator for DNA Alignment
  Weihong Xu, Saransh Gupta, Niema Moshiri, and Tajana Rosing
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023

• [TACO 2024] Abakus: Accelerating k-mer Counting With Storage Technology
  Lingxi Wu, Minxuan Zhou, Weihong Xu, Ashish Venkat, Tajana Rosing, and Kevin Skadron
  ACM Transactions on Architecture and Code Optimization (TACO), 2024

📊 Hyperdimensional Computing

• [TCAD 2024] Tri-HD: Train, Re-train, and Infer with Hyperdimensional Computing in Memory
  Weihong Xu, Saransh Gupta, Justin Morris, Xincheng Shen, Mohsen Imani, Baris Aksanli, and Tajana Rosing
  Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024
• [ESSERC 2024] FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
  Haichao Yang, Chang Eun Song, Weihong Xu, Behnam Khaleghi, Uday Mallappa, Monil Shah, Keming Fan, Mingu Kang, and Tajana Rosing
  to be presented at European Solid-State Electronics Research Conference (ESSERC), 2024
• [DATE 2024] AttBind: Memory-Efficient Acceleration for Long-Range Attention Using Vector-Derived Symbolic Binding
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  Design, Automation and Test in Europe Conference (DATE), 2024
• [ICCD 2023] HyperMetric: Robust Hyperdimensional Computing on Error-prone Memories using Metric Learning
  Weihong Xu, Viji Swaminathan, Sumukh Pinge, Sean Fuhrman, and Tajana Rosing
  IEEE International Conference on Computer Design (ICCD), 2023
• [DATE 2023] FSL-HD: Accelerating Few-Shot Learning on ReRAM using Hyperdimensional Computing
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  Design, Automation and Test in Europe Conference (DATE), 2023

💫 In/Near-memory Acceleration

• [Under Review] SLIM: A Heterogeneous Accelerator for Edge Inference of Sparse Large Language Model via Adaptive Thresholding
  Weihong Xu, Haein Choi, Po-Kai Hsu, Shimeng Yu, and Tajana Rosing
  Under review of ACM Transactions on Embedded Computing Systems, 2025
• [Under Review] Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND
  Weihong Xu, Junwei Chen, Po-Kai Hsu, Jaeyoung Kang, Minxuan Zhou, Sumukh Pinge, Shimeng Yu, and Tajana Rosing
  Under review of IEEE Transactions on Computers, 2024
• [HPCA 2022] TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformers
  Minxuan Zhou∗, Weihong Xu∗, Jaeyoung Kang, and Tajana Rosing
  IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022 * Equal contribution
• [DAC 2022] A Near-Storage Framework for Boosted Data Preprocessing of Mass Spectrum Clustering
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  IEEE/ACM Design Automation Conference (DAC), 2022

📝 Full Publication List

Under Review

• [Under Review] Proxima: Near-storage Acceleration for Graph-based Approximate Nearest Neighbor Search in 3D NAND
  Weihong Xu, Junwei Chen, Po-Kai Hsu, Jaeyoung Kang, Minxuan Zhou, Sumukh Pinge, Shimeng Yu, and Tajana Rosing
  Under review of IEEE Transactions on Computers, 2024

Published Journals

• [TCAD 2024] Tri-HD: Train, Re-train, and Infer with Hyperdimensional Computing in Memory
  Weihong Xu, Saransh Gupta, Justin Morris, Xincheng Shen, Mohsen Imani, Baris Aksanli, and Tajana Rosing
  Accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024
• [Bioinformatics 2024] HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors
  Weihong Xu, Po-kai Hsu, Niema Moshiri, Shimeng Yu, and Tajana Rosing
  Bioinformatics, 2024
  
• [TACO 2024] Abakus: Accelerating k-mer Counting With Storage Technology
  Lingxi Wu, Minxuan Zhou, Weihong Xu, Ashish Venkat, Tajana Rosing, and Kevin Skadron
  ACM Transactions on Architecture and Code Optimization (TACO), 2024
• [TCAD 2023] RAPIDx: High-performance ReRAM Processing in-Memory Accelerator for DNA Alignment
  Weihong Xu, Saransh Gupta, Niema Moshiri, and Tajana Rosing
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023
• [JPR 2023] HyperSpec: Fast Mass Spectra Clustering in Hyperdimensional Space
  Weihong Xu, Jaeyoung Kang, Wout Bittremieux, Niema Moshiri, and Tajana Rosing
  Journal of Proteome Research, 2023
  
• [TCAD 2020] Reconfigurable and Low-complexity Accelerator for Convolutional and Generative Networks over Finite Fields
  Weihong Xu, Zaichen Zhang, Xiaohu You, and Chuan Zhang
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020
• [JETCAS 2020] Deep Learning-aided Belief Propagation Decoder for Polar Codes
  Weihong Xu, Xiaosi Tan, Yair Be’ery, Zaichen Zhang, Xiaohu You, and Chuan Zhang
  IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2020

Published Conferences

• [DATE 2024] AttBind: Memory-Efficient Acceleration for Long-Range Attention Using Vector-Derived Symbolic Binding
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  Design, Automation and Test in Europe Conference (DATE), 2024
• [ESSERC 2024] FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
  Haichao Yang, Chang Eun Song, Weihong Xu, Behnam Khaleghi, Uday Mallappa, Monil Shah, Keming Fan, Mingu Kang, and Tajana Rosing
  to be presented at European Solid-State Electronics Research Conference (ESSERC), 2024
• [ICCD 2023] HyperMetric: Robust Hyperdimensional Computing on Error-prone Memories using Metric Learning
  Weihong Xu, Viji Swaminathan, Sumukh Pinge, Sean Fuhrman, and Tajana Rosing
  IEEE International Conference on Computer Design (ICCD), 2023
• [DATE 2023] FSL-HD: Accelerating Few-Shot Learning on ReRAM using Hyperdimensional Computing
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  Design, Automation and Test in Europe Conference (DATE), 2023
• [HPCA 2022] TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformers
  Minxuan Zhou∗, Weihong Xu∗, Jaeyoung Kang, and Tajana Rosing
  IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022 * Equal contribution
• [DAC 2022] A Near-Storage Framework for Boosted Data Preprocessing of Mass Spectrum Clustering
  Weihong Xu, Jaeyoung Kang, and Tajana Rosing
  IEEE/ACM Design Automation Conference (DAC), 2022