Yawei Li

Democratizing AI for pervasive applications.

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School of EEE, NTU Singapore

50 Nanyang Avenue, Singapore 639798

– Think big, aim high;

– Start small, stay patient;

– Never stop, never rush

I am a Nanyang Assistant Professor at the School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, where I lead the Deep Efficiency Lab. Previously, I was a Lecturer and Established Researcher at Integrated Systems Laboratory, ETH Zürich, where I worked with Prof. Luca Benini on AI accelerators and foundation models. I got my PhD degree from the Computer Vision Lab at ETH Zürich, supervised by Prof. Luc Van Gool.

I also collaborated with Prof. Radu Timofte, Dr. Michele Magno, and Prof. Ming-Hsuan Yang. During my PhD, I was mentored by Babak Ehteshami Bejnordi, Tijmen Blankevoort, and Amir Habibian at Qualcomm and Rakesh Ranjan at Meta.

Join the Deep Efficiency Lab

I am recruiting PhD students for the Fall 2027 and Spring 2028 intakes, as well as research fellows, research associates, and NTU project students (FYP, URECA, MSc by Dissertation).

Open positions →

Research

My research focuses on AI efficiency: accelerating computation, reducing memory footprint, and mitigating power consumption in AI systems, across both cloud and edge environments. Efficiency is rarely won at a single layer; the largest gains come from evolving the algorithm and the hardware that runs it together.

Applications Vision Language Embodied AI Biosignals
Efficient model designTransformers, state-space models, foundation models

What is the right computational mechanism for a given architecture? I have studied this for CNNs, graph neural networks, vision Transformers (LocalViT, GRL, SemanIR, VRT), and state-space models (MambaIR, FEMBA).

Model compression & deploymentQuantization, pruning, low-rank adaptation, token pruning

How much of a trained model is actually needed? My work spans weight pruning (group sparsity, random pruning, DHP), token pruning (FastVAR), quantization (SliM-LLM, OBR), tensor decomposition (learning filter basis), and low-rank adaptation (IntLoRA), applied to LLMs and diffusion models.

Software–hardware co-designDataflow, kernels, and scheduling from clusters to the edge

Once the model is fixed, the bottleneck moves to how it is mapped onto silicon. I work on serving modern models efficiently both on large clusters (FlatAttention) and on severely constrained edge devices (smart glasses, Retina, TinyTracker).

Hardware architecture & EDARISC-V processors, AI accelerators, PPA optimization, 3D integration

Open-source processor and AI accelerator architectures based on RISC-V, as well as EDA methodologies for hardware PPA (Power, Performance, Area) optimization and 3D integration. This is the newest direction in the lab, growing out of my work at ETH Zürich with Prof. Luca Benini's group.

Foundation models for biosignalsApplication focus · EEG, EMG, ECG, ultrasound

Biosignals record physiological activity and underpin healthcare, disease monitoring, BCI, HCI and AR/VR. They demand accurate modelling under a tight on-device compute budget — here efficiency is not an optimization but a precondition. This is my most active recent direction (PhysioWave, LUNA, WaveFormer, FEMBA, CEReBrO).

News

Nov 10, 2026 PhysioWave-T is released on arxiv! PhysioWave-T is a token-efficient wavelet foundation model for biosignals. Congrats Yanlong!
Oct 15, 2026 I will serve as an Area Chair for ICML 2027.
Sep 28, 2026 PrismQuant is released on arxiv! In this work, we derive optimal null-space rotations for grouped quantizers. Congrats Yanlong!
Sep 25, 2026 One work on floating-point microformat quantization and pruning for efficient MU-MIMO neural receivers is released on arxiv.
Sep 21, 2026 Our paper “TinyMyo: A Tiny Foundation Model for Flexible EMG Signal Processing at the Edge” is accepted to IEEE Transactions on Biomedical Engineering (TBME)! Congrats Matteo!
Sep 21, 2026 Our paper “SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving” is accepted to EuroSys 2027!
Aug 15, 2026 I am invited to serve as an Area Chair for ICLR 2027.

Selected Publications

  1. Arxiv
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    PrismQuant: Optimal Null-Space Rotations for Grouped Quantizers
    Yanlong Chen, Yining Chen, Song Zhang, Amirhossein Habibian, and Yawei Li
    arXiv preprint arXiv:2609.32429, 2026
  2. ICML
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    Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
    Yanlong Chen, Amirhossein Habibian, Luca Benini, and Yawei Li
    In Proceedings of International Conference on Machine Learning (ICML), 2026
  3. ICLR
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    Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs
    Hang Guo, Luca Benini, and Yawei Li
    In The Fourteenth International Conference on Learning Representations (ICLR), 2026
  4. NeurIPS
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    PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
    Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa, Simone Benatti, Luca Benini, and Yawei Li
    In Advances in Neural Information Processing Systems (NeurIPS), 2025
  5. NeurIPS
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    LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
    Berkay Döner, Thorir Mar Ingolfsson, Luca Benini, and Yawei Li
    In Advances in Neural Information Processing Systems (NeurIPS), 2025
  6. ICCV
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    FastVAR: Linear Visual Autoregressive Modeling via Cached Token Pruning
    Hang Guo, Yawei Li, Taolin Zhang, Jiangshan Wang, Tao Dai, Shu-Tao Xia, and Luca Benini
    In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025
  7. ISVLSI
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    FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Efficient Multi-Head Attention on Tile-Based Many-PE Accelerators
    Chi Zhang, Luca Colagrande, Renzo Andri, Thomas Benz, Gamze Islamoglu, Alessandro Nadalini, Francesco Conti, Yawei Li, and Luca Benini
    In IEEE Computer Society Annual Symposium on VLSI, 2025
  8. CVPR
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    MambaIRv2: Attentive State Space Restoration
    Hang Guo, Yong Guo, Yaohua Zha, Yulun Zhang, Wenbo Li, Tao Dai, Shu-Tao Xia, and Yawei Li
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025
  9. NeurIPS
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    Sharing Key Semantics in Transformer Makes Efficient Image Restoration
    Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc Van Gool, Ming-Hsuan Yang, and Nicu Sebe
    In Advances in Neural Information Processing Systems, 2024
  10. ECCV
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    Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
    Julian Moosmann, Pietro Bonazzi, Yawei Li, Sizhen Bian, Philipp Mayer, Luca Benini, and Michele Magno
    In Proceedings of the European conference on computer vision Workshops, 2024
  11. CVPR
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    Retina: Low-Power Eye Tracking with Event Camera and Spiking Hardware
    Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis, Yawei Li, Sadique Sheik, and Michele Magno
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024
  12. IROS
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    LocalViT: Analyzing Locality in Vision Transformers
    Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, Michele Magno, Luca Benini, and Luc Van Gool
    In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023
  13. IEEE Sensors
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    TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision for In-Sensor Gaze Estimation
    Pietro Bonazzi, Thomas Ruegg, Sizhen Bian, Yawei Li, and Michele Magno
    In Proceedings of IEEE Sensors, 2023
  14. CVPR
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    Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
    Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  15. CVPR
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    Revisiting Random Channel Pruning for Neural Network Compression
    Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, and Luc Van Gool
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
  16. ECCV
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    DHP: Differentiable Meta Pruning via HyperNetworks
    Yawei Li, Shuhang Gu, Kai Zhang, Luc Van Gool, and Radu Timofte
    In Proceedings of the European Conference on Computer Vision, 2020
  17. CVPR
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    Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
    Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, and Radu Timofte
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020