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Early-Bird-Tickets
[ICLR 2020] Drawing Early-Bird Tickets: Toward More Efficient Training of Deep NetworksHW-NAS-Bench
[ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search BenchmarkViTCoD
[HPCA 2023] ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-DesignShiftAddLLM
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less ReparameterizationShiftAddNet
[NeurIPS 2020] ShiftAddNet: A Hardware-Inspired Deep NetworkAutoDNNchip
BNS-GCN
[MLSys 2022] "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling" by Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan LinDepthShrinker
[ICML 2022] "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks", by Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan LinGCoD
[HPCA 2022] GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-DesignPatch-Fool
[ICLR 2022] "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?" by Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan LinCPT
[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan LinShiftAddViT
[NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformermg-verilog
Castling-ViT
[CVPR 2023] Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention During Vision Transformer InferenceLinearized-LLM
[ICML 2024] When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language ModelsEdge-LLM
[DAC 2024] EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and VotingDNN-Chip-Predictor
[ICASSP'20] DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware ArchitecturesE2Train
[NeurIPS 2019] E2-Train: Training State-of-the-art CNNs with Over 80% Less EnergySuperTickets
[ECCV 2022] SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter PruningViTALiTy
ViTALiTy (HPCA'23) Code RepositoryACT
[ICML 2024] Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention CalibrationLLM4HWDesign_Starting_Toolkit
LLM4HWDesign Starting ToolkitAuto-NBA
[ICML 2021] "Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators" by Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan LinS3-Router
[NeurIPS 2022] "Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing" by Yonggan Fu, Yang Zhang, Kaizhi Qian, Zhifan Ye, Zhongzhi Yu, Cheng-I Lai, Yingyan LinShiftAddNAS
[ICML 2022] ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural NetworksNeRFool
[ICML 2023] "NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations" by Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan (Celine) LinRobust-Scratch-Ticket
[NeurIPS 2021] "Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks" by Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan LinDouble-Win-Quant
[ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inference" by Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan LinFracTrain
[NeurIPS 2020] "FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training" by Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lintorchshiftadd
An open-sourced PyTorch library for developing energy efficient multiplication-less models and applications.HALO
The official code for [ECCV2020] "HALO: Hardware-aware Learning to Optimize"NASA
[ICCAD 2022] NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid NetworksSACoD
[ICCV 2021] "SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam" by Yonggan Fu, Yang Zhang, Yue Wang, Zhihan Lu, Vivek Boominathan, Ashok Veeraraghavan, Yingyan LinTinyML-Contest-Solution
TinyML2023EIC-Gatech-Open
Early-Bird-GCN
[AAAI 2022] Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery TicketsHint-Aug
EyeCoD
[ISCA 2022] EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-DesignOmni-Recon
[ECCV 2024 Oral] "Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields" by Yonggan Fu, Huaizhi Qu, Zhifan Ye, Chaojian Li, Kevin Zhao, and Yingyan (Celine) LinInstantNet
[DAC 2021] "InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks" by Yonggan Fu, Zhongzhi Yu, Yongan Zhang, Yifan Jiang, Chaojian Li, Yongyuan Liang, Mingchao Jiang, Zhangyang Wang, Yingyan LinSpline-EB
[TMLR] Max-Affine Spline Insights Into Deep Network PruningLove Open Source and this site? Check out how you can help us