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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 TransformerPipeGCN
[ICLR 2022] "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication" by Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Linmg-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 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