Yingyan Celine Lin
Associate Professor
Leads the EIC Lab on efficient machine learning through co-design of algorithms, accelerators and chips.
Yingyan Celine Lin's lab at Georgia Tech works on accelerators, chips & architecture and efficient training & inference. 45 papers since 2023, 36 at top venues, mostly DAC and CVPR. 15 alumni. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 80%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 15
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 13 papers2025 · T1 11
- 13 papers2024 · T1 9
- 19 papers2023 · T1 16
T1 venuesOtherVenues: DAC, CVPR, HPCA, ICML, NeurIPS
Research focuspaper titles that use each topic's words
- Efficient training & inference21 papers
- Accelerators, chips & architecture4 papers
- co-design3 papers
- efficient machine0 papers
- chips0 papers
Paper list
From the lab homepage.
202513 papers
- HPCA (T1)A Unified Accelerator for Real-Time Rendering Across Diverse Neural Renderers (opens in a new tab)
- CVPR (T1)Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training (opens in a new tab)
- ICCV (T1)Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deployment (opens in a new tab)
- DAC (T1)GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting (opens in a new tab)
- HPCA (T1)Gaussian Blending Unit: An Edge GPU Plug-in for Real-Time Gaussian-Based Rendering in AR/VR (opens in a new tab)
- ICLR (T1)Hymba: A Hybrid-head Architecture for Small Language Models (opens in a new tab)
- ACL (T1)LAMB: A Training-Free Method to Enhance the Long-Context Understanding of SSMs via Attention-Guided Token Filtering (opens in a new tab)
- CVPR (T1)Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers (opens in a new tab)
- ICLR (T1)LongMamba: Enhancing Mamba's Long-Context Capabilities via Training-Free Receptive Field Enlargement (opens in a new tab)
- TCADRe-CATA: Real-Time and Flexible Accelerator Design Framework for On-device Codec Avatars (opens in a new tab)
- CoRL (T1)SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies (opens in a new tab)
- DAC (T1)Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling (opens in a new tab)
202413 papers
- NeurIPS (T1)3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning (opens in a new tab)
- DAC (T1)3D-Carbon: An Analytical Carbon Modeling Tool for 3D and 2.5D Integrated Circuits
- NeurIPS (T1)AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment (opens in a new tab)
- TCADAutoAI2C: An Automated Hardware Generator for DNN Acceleration on both FPGA and ASIC (opens in a new tab)
- DAC (T1)EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Unified Compression and Adaptive Layer Voting (opens in a new tab)
- MICRO (T1)Fusion-3D: Integrated Acceleration for Instant 3D Reconstruction and Real-Time Rendering (opens in a new tab)
- 3DVMixRT: Mixed Neural Representations For Real-Time NeRF Rendering (opens in a new tab)
- ECCV (T1)Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields (opens in a new tab)
- NeurIPS (T1)ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization (opens in a new tab)
- ISPASSTowards Cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI (opens in a new tab)
- TCASAITowards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
- ICML (T1)Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration (opens in a new tab)
202319 papers
- TECSAn Investigation on Hardware-Aware Vision Transformer Scaling (opens in a new tab)
- TinyMLAugViT: Improving Vision Transformer Training by Marrying Attention and Data Augmentation
- CVPR (T1)Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence (opens in a new tab)
- CVPR (T1)Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention During Vision Transformer Inference (opens in a new tab)
- ICASSP (T1)ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement (opens in a new tab)
- MICRO TopPickEyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm/Hardware Co-Design (opens in a new tab)
- ISCA (T1)Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design (opens in a new tab)
- ICCAD (T1)GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models (opens in a new tab)
- CVPR (T1)Hint-Aug: Drawing Hints from Vision Foundation Models towards Boosted Few-shot Parameter-Efficient ViT Tuning (opens in a new tab)
- ISCA (T1)Instant-3D: Instant Neural Radiance Fields Training Towards Real-Time AR/VR 3D Reconstruction (opens in a new tab)
- DAC (T1)Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing (opens in a new tab)
- ICML (T1)Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modularized Learning (opens in a new tab)
Where alumni went15 listed
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USUIUC
Yongjoo Park
- Efficient training & inference
- USUCSD
Bill Lin
- Accelerators, chips & architecture
- SGNUS
Yao Lu
- Distributed, cloud & edge systems
- USMIT
Song Han
- Efficient training & inference
- USUT Austin
Diana Marculescu
- Accelerators, chips & architecture
- USUSC
Priyadarshini Panda
- Accelerators, chips & architecture
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Georgia Techcloses