Publications (Past 5 Years)

> 28,000 Citation with H-index 81 According to Google Scholar

For a full list, please download the CV.

2025

  • Xuan Shen, Peiyan Dong, Zhenglun Kong, Yifan Gong, Changdi Yang, Zhaoyang Han, Yanyue Xie, Lei Lu, Cheng Lyu, Chao Wu, Yanzhi Wang, Pu Zhao, "Squat: Quant Small Language Models on the Edge", in IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2025.
  • Xiaomeng Yang, Jian Gao, Yanzhi Wang, Xuan Zhang, "Zerosim: Zero-shot analog circuit evaluation with unified transformer embeddings", in IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2025.
  • Qitao Tan, Sung-En Chang, Rui Xia, Huidong Ji, Chence Yang, Ci Zhang, Jun Liu, Zheng Zhan, Zhenman Fang, Zhuo Zou, Yanzhi Wang, Jin Lu, Geng Yuan, "Perturbation-efficient zeroth-order optimization for hardware-friendly on-device training", in IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2025.
  • Wei Niu, Mengshu Sun, Zhengang Li, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Jun Liu, Mei Zhang, Yanzhi Wang, Xue Lin, Bin Ren, "Mobile-3dcnn: An acceleration framework for ultra-real-time execution of large 3d cnns on mobile devices", in ACM Transactions on Architecture and Code Optimization, 2025.
  • Z Kong, D Xu, Z Li, P Dong, H Tang, Y Wang, S Mukherjee, "Autovit: Achieving real-time vision transformers on mobile via latency-aware coarse-to-fine search", in International Journal of Computer Vision, 2025.
  • Changdi Yang, Zheng Zhan, Ci Zhang, Yifan Gong, J Liu, X Shen, H Tang, G Yuan, P Zhao, X Lin, W Yanzhi, "Fairsmoe: Mitigating multi-attribute fairness problem with sparse mixture-of-experts", in 34th International Joint Conference on Artificial Intelligence, 2025.
  • Cihan Ruan, Lei Lu, Rongduo Han, Wei Jiang, Wei Wang, Haoyu Wu, Qiming Yuan, Yanting Guo, Yanzhi Wang, Nam Ling, "HDCompression-DNA: Hybrid-Diffusion Neural Image Compression via DNA Storage", in IEEE International Conference on Multimedia and Expo (ICME), 2025.
  • Ci Zhang, Chence Yang, Qitao Tan, Jun Liu, Ao Li, Yanzhi Wang, Jin Lu, Jinhui Wang, Geng Yuan, "Towards memory-efficient and sustainable machine unlearning on edge using zeroth-order optimizer", in Great Lakes Symposium on VLSI, 2025.
  • Zhengang Li, Hongwu Peng, Xuan Shen, Masoud Zabihi, Xi Xie, Geng Yuan, Yanzhi Wang, Olivia Chen, Caiwen Ding, "Graph Convolutional Network Acceleration Using Adiabatic Superconductor Josephson Devices", in 39th ACM International Conference on Supercomputing, 2025.
  • Xianglu Shen, Huixin Zhang, Yanzhi Wang, Qi R Wang, "Unveiling the dynamics of human mobility in response to wildfire-induced air quality degradation: an examination of the 2019 Kincade fire", in Journal of Management in Engineering, 2025.
  • Jun Liu, Zhenglun Kong, Pu Zhao, Changdi Yang, Xuan Shen, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang, "Toward adaptive large language models structured pruning via hybrid-grained weight importance assessment", in AAAI Conference on Artificial Intelligence, 2025.
  • Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Jing Liu, Ruiyi Zhang, Ryan A Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, Jiuxiang Gu, "Numerical pruning for efficient autoregressive models", in AAAI Conference on Artificial Intelligence, 2025.
  • Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Yanyu Li, Yifan Gong, Kai Zhang, Hao Tan, Jason Kuen, Henghui Ding, Zhihao Shu, Wei Niu, Pu Zhao, Yanzhi Wang, Jiuxiang Gu, "Lazydit: Lazy learning for the acceleration of diffusion transformers", in AAAI Conference on Artificial Intelligence, 2025.
  • Lixia Han, Yiyang Chen, Siyuan Chen, Haozhang Yang, Ao Shi, Guihai Yu, Jiaqi Li, Zheng Zhou, Yijiao Wang, Yanzhi Wang, Xiaoyan Liu, Jinfeng Kang, Peng Huang, "CIMUS: 3D-stacked Computing-in-Memory Under Image Sensor Architecture for Efficient Machine Vision", in IEEE Transactions on Computers, 2025.
  • Jun Liu, Zhenglun Kong, Peiyan Dong, Xuan Shen, Pu Zhao, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang, "Rora: Efficient fine-tuning of llm with reliability optimization for rank adaptation", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025.
  • Xuan Shen, Hangyu Zheng, Yifan Gong, Zhenglun Kong, Changdi Yang, Zheng Zhan, Yushu Wu, Xue Lin, Yanzhi Wang, Pu Zhao, Wei Niu, "Sparse learning for state space models on mobile", in The Thirteenth International Conference on Learning Representations, 2025.
  • Dan Wu, Yanzhi Wang, Yuqi Fei, Guowang Gao, "A novel mixed-precision quantization approach for cnns", in IEEE Access, 2025.
  • Pinrui Yu, Zhenglun Kong, Pu Zhao, Peiyan Dong, Hao Tang, Fei Sun, Xue Lin, Yanzhi Wang, "Q-TempFusion: Quantization-Aware Temporal Multi-Sensor Fusion on Bird's-Eye View Representation", in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
  • Yuguang Yao, Jiancheng Liu, Yifan Gong, Xiaoming Liu, Yanzhi Wang, Xue Lin, Sijia Liu, "Can Adversarial Examples be Parsed to Reveal Victim Model Information?", in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
  • Chao Wu, Cheng Ji, Li-Pin Chang, Zongwei Zhu, Congming Gao, Weichao Guo, Chao Yu, Yanzhi Wang, "MedFS: Pursuing Low Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Device", in 23rd USENIX Conference on File and Storage Technologies (FAST 25), 2025.
  • Xuan Shen, Weize Ma, Jing Liu, Changdi Yang, Rui Ding, Quanyi Wang, Henghui Ding, Wei Niu, Yanzhi Wang, Pu Zhao, Jun Lin, Jiuxiang Gu, "Quartdepth: Post-training quantization for real-time depth estimation on the edge", in Computer Vision and Pattern Recognition Conference, 2025.
  • Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu, Anil Kag, Yang Sui, Huseyin Coskun, Ke Ma, Aleksei Lebedev, Ju Hu, Dimitris N Metaxas, Yanzhi Wang, Sergey Tulyakov, Jian Ren, "Snapgen-v: Generating a five-second video within five seconds on a mobile device", in Computer Vision and Pattern Recognition Conference, 2025.

2024

  • Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang, "Search for efficient large language models", in Advances in Neural Information Processing Systems, 2024.
  • Zheng Zhan, Zhenglun Kong, Yifan Gong, Yushu Wu, Zichong Meng, Hangyu Zheng, Xuan Shen, Stratis Ioannidis, Wei Niu, Pu Zhao, Yanzhi Wang, "Exploring token pruning in vision state space models", in Advances in Neural Information Processing Systems, 2024.
  • Zheng Zhan, Yushu Wu, Yifan Gong, Zichong Meng, Zhenglun Kong, Changdi Yang, Geng Yuan, Pu Zhao, Wei Niu, Yanzhi Wang, "Fast and memory-efficient video diffusion using streamlined inference", in Advances in Neural Information Processing Systems, 2024.
  • Hao Zhang, Malith Jayaweera, Bin Ren, Yanzhi Wang, Sucheta Soundarajan, "Reducing Unfairness in Distributed Community Detection", in IEEE International Conference on Data Mining (ICDM), 2024.
  • Peiyan Dong, Jinming Zhuang, Zhuoping Yang, Shixin Ji, Yanyu Li, Dongkuan Xu, Heng Huang, Jingtong Hu, Alex K Jones, Yiyu Shi, Yanzhi Wang, Peipei Zhou, "EQ-ViT: Algorithm-hardware co-design for end-to-end acceleration of real-time vision transformer inference on Versal ACAP architecture", in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024.
  • Jun Liu, Zhenglun Kong, Pu Zhao, Weihao Zeng, Hao Tang, Xuan Shen, Changdi Yang, Wenbin Zhang, Geng Yuan, Wei Niu, Xue Lin, Yanzhi Wang, "Tsla: A task-specific learning adaptation for semantic segmentation on autonomous vehicles platform", in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024.
  • Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang, "Rethinking token reduction for state space models", in Conference on Empirical Methods in Natural Language Processing, 2024.
  • Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin, "Pruning foundation models for high accuracy without retraining", in Findings of the Association for Computational Linguistics: EMNLP, 2024.
  • Xuan Shen, Zhaoyang Han, Lei Lu, Zhenglun Kong, Peiyan Dong, Zhengang Li, Yanyue Xie, Chao Wu, Miriam Leeser, Pu Zhao, Xue Lin, Yanzhi Wang, "Hotaq: Hardware oriented token adaptive quantization for large language models", in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024.
  • Lei Lu, Yanyue Xie, Wei Jiang, Wei Wang, Xue Lin, Yanzhi Wang, "Hybridflow: Infusing continuity into masked codebook for extreme low-bitrate image compression", in 32nd ACM International Conference on Multimedia, 2024.
  • Chao Wu, Yifan Gong, Liangkai Liu, Mengquan Li, Yushu Wu, Xuan Shen, Zhimin Li, Geng Yuan, Weisong Shi, Yanzhi Wang, "Aye-edge: Automated deployment space search empowering accuracy yet efficient real-time object detection on the edge", in 43rd IEEE/ACM International Conference on Computer-Aided Design, 2024.
  • Yifan Gong, Zheng Zhan, Yanyu Li, Yerlan Idelbayev, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren, "Efficient training with denoised neural weights", in European Conference on Computer Vision, 2024.
  • Zichong Meng, Changdi Yang, Jun Liu, Hao Tang, Pu Zhao, Yanzhi Wang, "Instructgie: Towards generalizable image editing", in European Conference on Computer Vision, 2024.
  • Zichong Meng, Jie Zhang, Changdi Yang, Zheng Zhan, Pu Zhao, Yanzhi Wang, "Diffclass: Diffusion-based class incremental learning", in European Conference on Computer Vision, 2024.
  • Liangkai Liu, Yanzhi Wang, Weisong Shi, "CPT: A Configurable Predictability Testbed for DNN Inference in Avs", in Tsinghua Science and Technology, 2024.
  • Geng Yang, Yanyue Xie, Zhong Jia Xue, Sung-En Chang, Yanyu Li, Peiyan Dong, Jie Lei, Weiying Xie, Yanzhi Wang, Xue Lin, Zhenman Fang, "Sda: Low-bit stable diffusion acceleration on edge fpgas", in 34th International Conference on Field-Programmable Logic and Applications (FPL), 2024.
  • Pinrui Yu, Dan Luo, Timothy Rupprecht, Lei Lu, Zhenglun Kong, Pu Zhao, Yanyu Li, Octavia I Camps, Xue Lin, Yanzhi Wang, "FasterVD: On Acceleration of Video Diffusion Models", in International Joint Conference on Artificial Intelligence (IJCAI), 2024.
  • Yifan Gong, Yushu Wu, Zheng Zhan, Pu Zhao, Liangkai Liu, Chao Wu, Xulong Tang, Yanzhi Wang, "Lotus: learning-based online thermal and latency variation management for two-stage detectors on edge devices", in 61st ACM/IEEE Design Automation Conference, 2024.
  • Zhengang Li, Xuan Shen, Geng Yuan, Masoud Zabihi, Tomoharu Yamauchi, Yanzhi Wang, Olivia Chen, "Late Breaking Result: AQFP-aware Binary Neural Network Architecture Search", in 61st ACM/IEEE Design Automation Conference, 2024.
  • Zhengang Li, Alec Lu, Yanyue Xie, Zhenglun Kong, Mengshu Sun, Hao Tang, Peiyan Dong, Caiwen Ding, Xue Lin, Zhenman Fang, and Yanzhi Wang, "Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers", in Proc. of International Conference on Supercomputing (ICS), 2024.
  • Yanyu Li, et al., "TextCraft: Your text encoder can be image quality controller", in Proc. of Computer Vision and Pattern Recognition (CVPR), 2024.
  • Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu, and Yanzhi Wang, "SNED: Superposition network architecture search for efficient video diffusion model", in Proc. of Computer Vision and Pattern Recognition (CVPR), 2024.
  • Timothy Rupprecht et al., "Digital avatars: framework development and evaluation", in International Joint Conference on Artificial Intelligence (IJCAI), 2024.
  • Pinrui Yu, Timothy Rupprecht et al., "FasterVD: On Acceleration of Video Diffusion Models", in International Joint Conference on Artificial Intelligence (IJCAI), 2024.
  • Yifan Gong, Yushu Wu, Pu Zhao, Zheng Zhan, Liangkai Liu, Chao Wu, Xulong Tang, Yanzhi Wang, "Lotus: learning-based online thermal and latency variation management for two-stage detectors on edge devices", in Proc. of Design Automation Conference (DAC), 2024.
  • Zhengang Li et al., "LBR: And-NAS: AQFP-aware binary neural network search", in Proc. of Design Automation Conference (DAC), 2024.
  • Sheng Li, Geng Yuan, Yawen Wu, Yue Dai, Chao Wu, Alex Jones, Jingtong Hu, Yanzhi Wang, and Xulong Tang, "EdgeOL: Efficient in-situ online learning on edge devices", in Proc. of International Conference on Learning Representation (ICLR), 2024.
  • Xuan Shen, Peiyan Dong, et al., "Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge", in the AAAI Conference on Artificial Intelligence (AAAI), 2024.
  • Yushu Wu, Chao Wu, Geng Yuan, et al., "DACO: Pursuing Ultra-low Power Consumption via DNN-Adaptive CPU-GPU CO-optimization on Mobile Devices", in Proc. of the Design, Automation and Test in Europe Conference (DATE), 2024.
  • Yanyue Xie, Peiyan Dong, et al., "SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits", in Proc. of the Design, Automation and Test in Europe Conference (DATE), 2024.
  • Malith Jayweera, Yanyu Li, Bin Ren, David Kaeli, and Yanzhi Wang, "EFCON: Deformable Convolutions Leveraging Interval Search and GPU Texture Hardware", in Proc. of IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2024.
  • Malith Jayweera, Martin Kong, Yanzhi Wang, and David Kaeli, "Energy-aware tile size selection for affine programs on GPUs", in Proc. of Code Generation and Optimization Conference (CGO), 2024.
  • Husheng Han et al., "Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks", in IEEE Trans. on Computer Aided Design of Integrated Circuits and Systems (TCAD), 2024.

2023

  • Peiyan Dong, Lei Lu, Chao Wu, Cheng Lyu, Geng Yuan, Hao Tang, and Yanzhi Wang, "PackQViT: Faster sub-8-bit vision transformers via full and packed quantization on the mobile", in Proc. of Neural Processing Information Systems (NeurIPS), 2023.
  • Peiyan Dong, Zhenglun Kong, Xin Meng, Pinrui Yu, Yifan Gong, Geng Yuan, Hao Tang, and Yanzhi Wang, "HoTBEV: Hardware-oriented transformer-based multi-view 3D detector for BEV perception", in Proc. of Neural Processing Information Systems (NeurIPS), 2023.
  • Yanyu Li, Huan Wang, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang, Sergey Tulyakov, and Jian Ren, "SnapFusion: Text-to-image diffusion model on mobile devices within two seconds", in Proc. of Neural Processing Information Systems (NeurIPS), 2023.
  • Zhengang Li, Geng Yuan, Tomoharu Yamauchi, Massoud Zahibi, Yanyue Xie, Nobuyuki Yoshikawa, Devesh Tiwari, Olivia Chen, and Yanzhi Wang, "SupeRBNN: Randomized binary neural network using adiabatic superconductor Josephson devices", in 56th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2023.
  • Hao Zhang, Malith Jayaweera, Bin Ren, Yanzhi Wang, and Sucheta Soundarajan, "Unfairness in distributed graph frameworks", in Proc. of International Conference on Data Mining (ICDM), 2023.
  • Yanyu Li, Ju Hu, Yang Wen, Georgios Evangelidis, Kamyar Salahi, Yanzhi Wang, et al., "Rethinking vision transformers for MobileNet size and speed", in Proc. of International Conference on Computer Vision (ICCV), 2023.
  • Changdi Yang, Yi Sheng, Peiyan Dong, Zhenglun Kong, Yanyu Li, Pinrui Yu, Lei Yang, Xue Lin, and Yanzhi Wang, "Fast and fair medical AI on the edge through neural architecture search for hybrid vision models", in Proc. of International Conference on Computer Aided Design (ICCAD), 2023.
  • Yushu Wu, Chao Wu, Yifan Gong, Zheng Zhan, Geng Yuan, Yanyu Li, Qi Wang, and Yanzhi Wang, "MOC: Multi-objective mobile CPU-GPU co-optimization for power-efficient DNN inference", in Proc. of International Conference on Computer Aided Design (ICCAD), 2023.
  • Peiyan Dong, Zhenglun Kong, Xin Meng, et al., "SpeedDETR: Speed-aware Transformers for End-to-end Object Detection", in International Conference on Machine Learning (ICML), 2023.
  • Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, and Jennifer Dy, "DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning", in International Conference on Machine Learning (ICML), 2023.
  • Xuan Shen, Zhenglun Kong, Minghai Qin, et al., "Data Level Lottery Ticket Hypothesis for Vision Transformers", in International Joint Conference on Artificial Intelligence (IJCAI), 2023.
  • Xuan Shen, Yaohua Wang, Ming Lin, Dylan Huang, Hao Tang, Xinyu Sun, and Yanzhi Wang, "DeepMAD: Mathematical architecture design for deep convolutional neural network", to appear in Proc. of Computer Vision and Pattern Recognition (CVPR), 2023.
  • Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang, Yao Li, Caiwen Ding, Yanzhi Wang, Yi Liang, and Dongkuan Xu, "You need multiple exiting: dynamic early exiting for accelerating unified vision language model", to appear in Proc. of Computer Vision and Pattern Recognition (CVPR), 2023.
  • Changdi Yang, Pu Zhao, Yanyu Li, Wei Niu, Jiexiong Guan, et al., "Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge", to appear in Proc. of Computer Vision and Pattern Recognition (CVPR), 2023.
  • Yifan Gong, Pu Zhao, Zheng Zhan et al., "Condense: A framework for device and frequency adaptive neural network models on the edge", in Proc. of Design Automation Conference (DAC), 2023.
  • Zhengang Li, Yanyue Xie, Xue Lin and Yanzhi Wang, "Ubiquitous deep learning acceleration on the edge", in Proc. of Design Automation Conference (DAC), 2023.
  • Yifan Gong, Yuguang Yao, Yize Li, et al., "Reverse engineering of imperceptible adversarial image pertubations", in Proc. of International Conference on Learning Representation (ICLR), 2023.
  • Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong et al., "Self-ensemble protection” training check-points are good data protectors", in Proc. of International Conference on Learning Representation (ICLR), 2023.
  • Sheng Li, Geng Yuan, Yue Dai, Youtao Zhang, Yanzhi Wang, and Xulong Tang, "SmartFRZ: An efficient training framework using attention-based layer freezing", in Proc. of International Conference on Learning Representation (ICLR), 2023.
  • Hongyu Li, Zhengang Li, Neset Akmandor, Huaizu Jiang, Yanzhi Wang, and Taskin Padir, "StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks", in International Conference on Robotic Automation (ICRA), 2023.
  • Yanyu Li, Changdi Yang, Pu Zhao, Geng Yuan, et al., "Towards real-time segmentation on the edge", in the AAAI Conference on Artificial Intelligence (AAAI), 2023.
  • Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, et al., "Peeling the onion: hierarchical reduction of data redundancy for efficient vision transformer training", in the AAAI Conference on Artificial Intelligence (AAAI), 2023.
  • Sung-En Chang, Geng Yuan, Alec Lu, Mengshu Sun, Yanyu Li, Xiaolong Ma, Zhengang Li, Yanyue Xie, Minghai Qin, Xue Lin, Zhenman Fang, and Yanzhi Wang, "ESRU: Extremely low-bit and hardware-efficient stochastic rounding unit design for 8-bit DNN training", in Proc. of the Design, Automation and Test in Europe Conference (DATE), 2023.
  • Peiyan Dong, Mengshu Sun, Alec Lu, et al., "HeatViT: Hardware-efficient adaptive token pruning for vision transformers", in Proc. of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2023.
  • Jou-An Chen, Wei Niu, Bin Ren, Yanzhi Wang, and Xipeng Shen, "Survey: Exploiting Data Redundancy for Optimization of Deep Learning", to appear in ACM Computing Surveys, 2023.
  • Yushuo Guan, Ning Liu, et al., "DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search", to appear in IEEE TNNLS, 2023.
  • Zeinab Jalali, Chenghong Wang, et al., "Memristor-based spectral decomposition of matrices and its applications" to appear in IEEE Trans, on Computers, 2023.
  • Yinan Tang, Tongtong Yuan, Zhiyuan Xu, Weiyi Zhang, Jian Tang, Guoliang Xue, and Yanzhi Wang, "AI-enabled experience-driving networking: vision, state-of-the-art and future directions", IEEE Network Magazine, 2023.
  • Chen Pan, Wen Zhang, Yanzhi Wang, and Mimi Xie, "ELIXIR: An expedient connection paradigm for self-powered IoT devices", in IEEE Trans. on Computer Aided Design of Integrated Circuits and Systems (TCAD), 2023.
  • Zhiyuan Xu, Dejun Yang, Chengxiang Yin, Jian Tang, Yanzhi Wang, and Guoliang Xue, "A co-scheduling framework for DNN models on mobile and edge devices with heterogeneous hardware", to appear in IEEE Trans. on Mobile Computing (TMC), 2023.

2022

  • Yanyu Li, Geng Yuan, et al., "Efficientformer: vision transformers at mobilenet speed", in Proc. of Neural Processing Information Systems (NeurIPS), 2022.
  • Yihua Zhang, Yuguang Yao, et al., "Advancing Model Pruning via Bi-level Optimization", in Proc. of Neural Processing Information Systems (NeurIPS), 2022.
  • Zifeng Wang, Zheng Zhan, et al., "SparCL: Sparse Continual Learning on the Edge", in Proc. of Neural Processing Information Systems (NeurIPS), 2022.
  • Geng Yuan, Yanyu Li, et al., "Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training", in Proc. of Neural Processing Information Systems (NeurIPS), 2022.
  • Liangkai Liu, Zheng Dong, Yanzhi Wang and Weisong Shi, "Prophet: Realizing a Predictable Real-time Perception Pipeline for Autonomous Vehicles", in Proc. of IEEE Real-Time Systems Symposium (RTSS), 2022.
  • Wei Niu, Jiexiong Guan, Xipeng Shen, Yanzhi Wang, Gagan Agrawal, Bin Ren, "GCD^2: A Globally Optimizing Compiler for Mapping DNNs to Mobile DSPs", in 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2022.
  • Zifeng Wang, Tong Jian, Jennifer Dy, Yanzhi Wang, and Stratis Ioannidis, "Pruning Adversarially Robust Neural Networks without Adversarial Examples", in Proc. of International Conference on Data Mining (ICDM), 2022.
  • Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, and Yanzhi Wang, "All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management", in Proc. of International Conference on Computer Aided Design (ICCAD), 2022.
  • Zhirui Hu, Peiyan Dong, Zhepeng Wang, Youzuo Lin, Yanzhi Wang, and Weiwen Jiang, "Quantum Neural Network Compression", in Proc. of International Conference on Computer Aided Design (ICCAD), 2022.
  • Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, and Yanzhi Wang, "Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution", in Proc. of European Conference on Computer Vision (ECCV), 2022.
  • Zhenglun Kong, Peiyan Dong, et al., "SPViT: Enabling Faster Vision Transformers via Soft Token Pruning", in Proc. of European Conference on Computer Vision (ECCV), 2022.
  • Geng Yuan, Sung-en Chang, et al., "You Already Have It: A Generator-Free Low-Precision DNN Training Framework using Stochastic Rounding", in Proc. of European Conference on Computer Vision (ECCV), 2022.
  • Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, and Zhangyang Wang, "Coarsening the granularity: towards structurally sparse lottery tickets", in International Conference on Machine Learning (ICML), 2022.
  • Yanyu Li, Pu Zhao, Geng Yuan, Xue Lin, Yanzhi Wang, and Xin Chen, "Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization", in International Joint Conference on Artificial Intelligence (IJCAI), 2022.
  • Pu Zhao et al., "Learning to generate image source-agnostic universal adversarial perturbations", in International Joint Conference on Artificial Intelligence (IJCAI), 2022.
  • Qing Jin, Jian Ren, Richard Zhaung, Sumant Hanumante, Zhengang Li, Zhiyu Chen, Kaiyuan Yang, Yanzhi Wang, and Sergey Tulyakov, "F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization", in Proc. of International Conference on Learning Representation (ICLR), 2022.
  • Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, and Yuan Xie, "Effective Model Sparsification by Scheduled Grow-and-Prune Methods", in Proc. of International Conference on Learning Representation (ICLR), 2022.
  • Yifan Gong, Yuguang Yao, et al., "Reverse Engineering of Imperceptible Adversarial Image Perturbations", in Proc. of International Conference on Learning Representation (ICLR), 2022.
  • Peiyan Dong, Yanyue Xie, Hongjia Li, Mengshu Sun, Olivia Chen, Nobuyuki Yoshikawa, and Yanzhi Wang, "TAAS: A Timing-Aware Analytical Strategy for AQFP-Capable Placement Automation", in Proc. of Design Automation Conference (DAC), 2022.
  • Mengshu Sun, Zhengang Li, et al., "FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization", in Proc. of Design Automation Conference (DAC) LBR, 2022.
  • Sung-en Chang, Geng Yuan, et al., "Hardware-efficient stochastic rounding unit design for DNN training", to appear in Proc. of Design Automation Conference (DAC) LBR, 2022.
  • Hsin-Hsuan Sung et al., "Enabling Level-4 Autonomous Driving on a Single $1k Off-the-Shelf Card", in Proc. of RTAS 2022 (Industry Paper).
  • Bingyao Li, Qi Xue, Geng Yuan, Sheng Li, Xiaolong Ma, Yanzhi Wang, and Xulong Tang, "Optimizing data layout for trainining deep neural networks", in the Proc. of WWW, 2022.
  • Mengshu Sun, Sheng Lin, Shan Liu, Wei Jiang, Wei Wang, Yanzhi Wang and Songnan Li, "Hardware-Friendly Acceleration for Deep Neural Networks with Micro-Structured Compression", in Proc. of FCCM 2022.
  • Zhiyu Chen, Qing Jin, Zhanghao Yu, Yanzhi Wang, and Kaiyuan Yang, "DCT-RAM: A Driver-Free Process-In-Memory 8T SRAM Macro with Multi-Bit Charge-Domain Computation and Time-Domain Quantization", in Proc. of CICC, 2022.
  • Nasim Soltani, Yanyu Li, Deniz Erdogmus, Yanzhi Wang, and Kaushik Chowdhurry, "NN-key: A neural network-based secret key for demapping OFDM symbols", in Proc. of CCNC, 2022.
  • Md. Oli-Uz-Zaman, Saleh Ahmad Khan, Geng Yuan, Yanzhi Wang, Zhiheng Liao, Jingyan Fu, Caiwen Ding, and Jinhui Wang, "Reliability improvement in RRAM-based DNN for edge computing", in Proc. of ISCAS, 2022.
  • Xiaolong Ma, Geng Yuan, Zhengang Li et al., "BLCR: Towards real-time DNN execution with block-based reweighted pruning", in Proc. of ISQED, 2022.
  • Mengshu Sun et al., "FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization", in Proc. of ACM International Symposium on Field Programmable Gate Arrays (FPGA), 2022.
  • Cheng Gong et al., "Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks" in IEEE TPDS, 2022.
  • Yifan Gong, Zheng Zhan, et al., "Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration", in IEEE TODAES, 2022.
  • Nasim Soltani et al., "Neural Network-based OFDM Receiver for Resource Constrained IoT Devices", in IEEE Internet of Things Magazine, 2022.
  • Runze Han, Peng Huang, Yachen Xiang et al., "Floating Gate Transistor-based Accurate Digital In-Memory Computing for Deep Neural Networks", in Advanced Intelligent Systems, 2022.
  • Bahar Azari, hai Cheng, Nasim Soltami et al., "Automated deep learning-based wide-band receiver", in Computer Networks, 2022.
  • Timothy Rupprecht and Yanzhi Wang, "A survey for deep reinforcement learning in markovian cyber-physical systems: Common problems and solutions", Elsevier Neural Networks Journal, 2022.
  • Fuxun Yu, Zirui Xu, Chenchen Liu, et al., "AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency", in IEEE Trans. on Computer Aided Design (TCAD), 2022.
  • Geng Yuan, Peiyan Dong, Mengshu Sun, et al., "Mobile or FPGA? A comprehensive evaluation on energy efficiency and a unified optimization framework", in ACM Trans. on Embedded Computing Systems (TECS), 2022.
  • Jingyu Wang, Songming Yu, Zhuqing Yuan, et al., "PACA: A Pattern Pruning Algorithm and Channel-Fused High PE Utilization Accelerator for CNNs", in IEEE Trans. on Computer Aided Design (TCAD), 2022.
  • Yixuan Hu et al., "A 28nm 198.9 TOPS/W Fault-Tolerant Stochastic Computing Neural Network Processor", in IEEE Solid-State Circuits Letters, 2022.
  • Chengxiang Yin, Jian Tang, Tongtong Yuan, Zhiyuan Xu, and Yanzhi Wang, "Bridging the gap between semantic segmentation and instance segmentation", in IEEE Trans. on Multimedia, 2022.
  • Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren, "GRIM: A general real-time deep learning inference framework for mobile devices based on fine-grained structured weight sparsity", in IEEE Trans. on Pattern Recognition and Machine Intelligence (TPAMI), 2022.
  • Tianyun Zhang, Shaokai Ye, Xiaoyu Feng, Xiaolong Ma, Kaiqi Zhang, Zhengang Li, Jian Tang, Sijia Liu, Xue Lin, Yongpan Liu, Makan Fardad, and Yanzhi Wang, "StructADMM: Achieving Ultra-High Efficiency in Structured Pruning for DNNs", in IEEE Trans. on Neural networks and Learning Systems (TNNLS), 2022.
  • Yanzhi Wang et al., "Non-Structured DNN Weight Pruning: Is it Beneficial in Any Problem?", in IEEE Trans. on Neural networks and Learning Systems (TNNLS), 2022.
  • Zhiyuan Xu, Jian Tang, Chengxiang Yin, Yanzhi Wang, Guoliang Xue, Jing Wang, and Mustafa Gursoy, "ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning", in IEEE Trans. on Mobile Computing (TMC), 2022.
  • Tong Jian, Zifeng Wang, Zheng Zhan, Nasim Soltani, Yifan Gong, Runbin Shi, Kaushik Chowdhury, Jennifer Dy, Yanzhi Wang, and Stratis Ioannidis, "Radio frequency fingerprinting on the edge", in IEEE Trans. on Mobile Computing (TMC), 2022.

2021

  • Geng Yuan, Xiaolong Ma, et al., "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", in Proc. of Neural Processing Information Systems (NeurIPS), 2021.
  • Xiaolong Ma, Geng Yuan, et al., "Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?", to appear in Proc. of Neural Processing Information Systems (NeurIPS), 2021.
  • Husheng Han, Kaidi Xu, Xing Hu, et al., "ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers", to appear in Proc. of Neural Processing Information Systems (NeurIPS), 2021.
  • Kaidi Xu et al., "Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification", to appear in Proc. of Neural Processing Information Systems (NeurIPS), 2021.
  • Sung-en Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, and Xue Lin, "RMSMP: A novel deep neural network quantization framework with row-wise mixed schemes and multiple precisions", to appear in Proc. of International Conference on Computer Vision (ICCV), 2021.
  • Fangxin Liu, Wenbo Zhao, Zhezhi He, Yanzhi Wang, Zongwu Wang, Changzhi Dai, Xiaoyao Liang, and Li Jiang, "Improving neural network efficiency via post-training quantization with adaptive floating-point", to appear in Proc. of International Conference on Computer Vision (ICCV), 2021.
  • Zheng Zhan, Yifan Gong, Pu Zhao, et al., "Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search", to appear in Proc. of International Conference on Computer Vision (ICCV), 2021.
  • Weizheng Xu, Ashutosh Pattnaik, Geng Yuan, Yanzhi Wang, Youtao Zhang, and Xulong Tang, "ScaleDNN: Data Movement Aware DNN Training on Multi-GPU", to appear in Proc. of International Conference on Computer Aided Design (ICCAD), 2021.
  • Ning Liu, Geng Yuan, Xiaolong Ma, Xuan Shen, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, and Yanzhi Wang, "Lottery ticket preserves weight correlation: Is it desirable or not?" to appear in Proc. of International Conference on Machine Learning (ICML), 2021.
  • Wei Niu, Jiexiong Guan, Gagan Agrawal, Yanzhi Wang, and Bin Ren, "DNNFusion: Accelerating deep neural networks execution with advanced operator fusion", to appear in Proc. of ACM International Conference on Programming Language Design and Implementation (PLDI), 2021.
  • Geng Yuan, Payman Behnam, Zhengang Li, Ali Shafiee, Sheng Lin, Xiaolong Ma, Hang Liu, Xuehai Qian, Mahdi Bojnordi, Yanzhi Wang, and Caiwen Ding, "FORMS: Fine-grained polarized ReRAM-based in-situ computation with mixed-signal DNN accelerator", in Proc. of International Symposium on Computer Architecture (ISCA), 2021.
  • Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, and Dingwen Tao, "ClickTrain: Efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning", in Proc. of International Conference on Supercomputing (ICS), 2021.
  • Zhengang Li et al., "NPAS: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration", in Proc. of Computer Vision and Pattern Recognition (CVPR), 2021.
  • Qing Jin, Jian Ren, Oliver Woodford, Jiazhuo Wang, Geng Yuan, Yanzhi Wang, and Sergey Tulyakov, "Teachers do more than teach: Compressing image-to-image models", to appear in Proc. of Computer Vision and Pattern Recognition (CVPR), 2021.
  • Pu Zhao, Geng Yuan, Yuxuan Cai, Wei Niu, Qi Liu, Wujie Wen, Bin Ren, Yanzhi Wang, and Xue Lin, "Neural pruning search for real-time object detection of autonomous vehicles", to appear in Proc. of Design Automation Conference (DAC), 2021.
  • Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Caiwen Ding, Makan Fardad, and Yanzhi Wang, "A unified DNN weight pruning framework using reweighted optimization methods", to appear in Proc. of Design Automation Conference (DAC), 2021.
  • Xuan Shen, Geng Yuan, Wei Niu, Bin Ren, and Yanzhi Wang, "Towards fast and accurate multi-person pose estimation on mobile devices", to appear in International Joint Conference on Artificial Intelligence (IJCAI), 2021.
  • Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Bin Ren, and Yanzhi Wang, "A compression-compilation framework for on-mobile real-time BERT applications", to appear in International Joint Conference on Artificial Intelligence (IJCAI), 2021.
  • Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, and Yanzhi Wang, "YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • Wei Niu, Mengshu Sun, Zhengang Li, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Xue Lin, Bin Ren, and Yanzhi Wang, "RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2021.
  • Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, and Yanzhi Wang, "A Compression-Compilation Co-Design Framework Towards Real-Time Object Detection on Mobile Devices", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI) (Demonstration Paper), 2021.
  • Pu Zhao et al., "Towards real-time 3D object detection for autonomous vehicles with pruning search", in Proc. of IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2021.
  • Jinliang Xie, Jie Tang, Yanzhi Wang, Qi Zhu, and Shaoshan Liu, "An infrastructure-aided high definition map data provisioning service for autonomous driving", in Proc. of IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2021.
  • Sung-en Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden So, Yanzhi Wang, Xuehai Qian, and Xue Lin, "Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework" in Proc. of High-Performance Computing Architecture (HPCA), 2021.
  • Geng Yuan, Yuxuan Cai, et al., "TinyADC: Peripheral Circuit-aware Weight Pruning Framework for Mixed-signal DNN Accelerators", in Proc. of Design Automation and Test in Europe (DATE), 2021.
  • Hongjia Li, Mengshu Sun, Tianyun Zhang, Olivia Chen, Nobuyuki Yoshikawa, Bei Yu, Yanzhi Wang, and Yibo Lin, "Towards AQFP-capable physical design automation", in Proc. of Design Automation and Test in Europe (DATE), 2021.
  • Teng Li, Zhiyuan Xu, Jian Tang, Kun Wu, and Yanzhi Wang, "EXTRA: An experience-driving control framework for distributed stream data processing with a variable number of threads", in Proc. of IEEE/ACM International Symposium on Quality of Service (IWQoS), 2021.
  • Qin Li et al., "A 22.3 nJ/frame low-memory beyond-real-time keyword-spotting chip with configurable feature extraction and distributed perceptual computation", in International Symposium on Solid-State Circuits (ISSCC) SRP, 2021.
  • Malith Jayaweera, Yanzhi Wang, and David Kaeli, "Data vs. instructions: runtime code generation for convolutions", in Proc. of IEEE/ACM International Symposium on Code Generation and Optimization (CGO) SRC, 2021.
  • Geng Yuan, Xiaolong Ma, Zhengang Li, Wei Niu, Bin Ren, Xue Lin, and Yanzhi Wang, "Memory-bounded sparse training on the edge", in HAET Workshop at International Conference on Learning Representation (ICLR), 2021.
  • Shaoshan Liu, Bin Ren, Xipeng Shen, Yanzhi Wang, "CoCoPIE: Enabling Real-Time AI on Off-the-Shelf Mobile Devices via Compression-Compilation Co-Design", in Communications of ACM (CACM), 2021.
  • Zhiyuan Xu, Dejun Yang, Jian Tang, Yinan Tang, Tongtong Yuan, Yanzhi Wang, and Guoliang Xue, "An actor-critic-based transfer learning framework for experience-driven networking", in IEEE/ACM Trans. on Networking (ToN), 2021.
  • Jinshan Yue et al., "STICKER-T: An Energy Efficient Neural Network Processor Using Block-Circulant Algorithm and Unified Frequency-Domain Acceleration", in IEEE Journal of Solid-State Circuits (JSSC), 2021.
  • Youwei Zhuo et al., "Distributed graph processing system and process-in-memory architecture with precise loop-carried dependency guarantee", in ACM Trans. on Computer Systems (TOCS), 2021.
  • Zhiyu Chen et al., "CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference", in IEEE Journal of Solid-State Circuits (JSSC), 2021.
  • Zhiyuan Xu, Kun Wu, Weiyi Zhang, Jian Tang, Yanzhi Wang, and Guoliang Xue, "PnP-DRL: a plug-and-play deep reinforcement learning approach for experience-driven networking", in IEEE Journal on Selected Areas in Communications (JSAC), 2021.
  • Qin Li et al., "NS-FDN: Near-Sensor processing architecture of Feature-configurable Distributed Network for beyond-real-time always-on keyword spotting", in IEEE Trans. on Circuits and Systems I (TCAS-I), 2021.

2020

  • Yi-Chen Chang, Hongjia Li, Olivia Chen, Yanzhi Wang, Nobuyuki Yoshikawa, and Tsung-yi Ho, "ASAP: An Analytical Strategy for AQFP Placement", in Proc. of International Conference on Computer-Aided Design (ICCAD), 2020.
  • Qi Liu, Wujie Wen, and Yanzhi Wang, "Concurrent Weight Encoding-based Detection for Bit-Flip Attack on Neural Network Accelerators", in Proc. of International Conference on Computer-Aided Design (ICCAD), 2020.
  • Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-yu Chen, Yanzhi Wang, and Xue Lin, "Adversarial T-shirt! evading person detectors in a physical world", in Proc. of European Conference on Computer Vision (ECCV), 2020.
  • Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang, "An Image Enhancing Pattern-based Sparsity for Real-Time Inference on Mobile Devices", in Proc. of European Conference on Computer Vision (ECCV), 2020.
  • Wei Niu, Mengshu Sun, Zhengang Li, Geng Yuan, Pu Zhao, Xue Lin, Yanzhi Wang, and Bin Ren, "Real-time 3D CNN Inference for Action Recognition on Mobile Devices", in European Conference on Computer Vision (ECCV), 2020.
  • Zheng Zhan, Pu Zhao, Geng Yuan, Wei Niu, Bin Ren, Xue Lin, and Yanzhi Wang, "Real-time DNN Accelerations on Mobile Devices for Versatile Practical Deep Learning Applications", in European Conference on Computer Vision (ECCV), 2020.
  • Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, and Stratis Ioannidis, "Learn-Prune-Share for Lifelong Learning", in International Conference on Data Mining (ICDM), 2020.
  • Shichao Xu, Yixuan Wang, Yanzhi Wang, Zheng O’Neill, and Qi Zhu, "One for many: transfer learning for building HVAC control", in Proc. of International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (Buildsys), 2020.
  • Masuma Akter Rumi, Xiaolong Ma, Yanzhi Wang, and Peng Jiang, "Accelerating Sparse CNN Inference on GPUs with Performance-Aware Weight Pruning", in Proc. of International Conference on Parallel Architectures and Compilation Techniques (PACT), 2020.
  • Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, and Bin Ren, "Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization" in Proc. of International Joint Conferences on Artificial Intelligence Organization (IJCAI), 2020.
  • Runbin Shi, Peiyan Dong, Tong Geng, Yuhao Ding, Xiaolong Ma, Martin Herbordt, Ang Li, Hayden So, and Yanzhi Wang, "CSB-RNN: A faster-than-realtime RNN Acceleration Framework with Compressed Structured Blocks", in Proc. of International Conference on Supercomputing (ICS), 2020.
  • Wei Niu, Xiaolong Ma, Sheng Lin, Shihao Wang, Xuehai Qian, Xue Lin, Yanzhi Wang, and Bin Ren, "PatDNN: Achieving real-time DNN execution on mobile devices with pattern-based weight pruning" in ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2020.
  • Chaoqun Chu, Yanzhi Wang, Yilong ZHao, Xiaolong Ma, Shaokai ye, Yunyan Hong, Xiaoyao Liang, Yinhe Han, Yun Chen, Xiaosong Cui, and Li Jiang, "PIM-Prune: Fine-grained DCNN pruning for crossbar-based process-in-memory architecture", in Design Automation Conference (DAC), 2020.
  • Zhanhong Tan, Jiebo Song, Xiaolong Ma, Sia-Huat Tan, Hongyang Chen, Yuanqing Miao, Yifu Wu, Shaokai Ye, Yanzhi Wang, Dehui Li, and Kaisheng Ma, "PCNN: Pattern-based fine-grained regular pruning towards optimizing CNN accelerators", in Design Automation Conference (DAC), 2020.
  • Peiyan Dong, Siyue Wang, Wei Niu, Chengming Zhang, Sheng Lin, Zhengang Li, Yifan Gong, Bin Ren, Xue Lin, Yanzhi Wang, Dingwen Tao, "RTMobile: Beyond Real-time Mobile Acceleration of RNNs for Speech Recognition", in Design Automation Conference (DAC), 2020.
  • Youwei Zhuo, Jingji Chen, Qinyi Luo, Yanzhi Wang, Hailong Yang, Depei Qian, and Xuehai Qian, "SympleGraph: Distributed graph processing with precise loop-carried dependency guarantee", in ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2020.
  • Fuxun Yu, Chenchen Liu, Di Wang, Yanzhi Wang, Xiang Chen, "AntiDOte: Attention-based dynamic optimization for neural network runtime efficiency", in Proc. of Design Automation and Test in Europe (DATE), 2020.
  • Yawen Zhang, Sheng Lin, Runsheng Wang, Yanzhi Wang, Yuan Wang, Weikang Qian, Ru Huang, "When Sorting Network Meets Parallel Bitstreams: A Fault-Tolerant Parallel Ternary Neural Network Accelerator based on Stochastic Computing", in Proc. of Design Automation and Test in Europe (DATE), 2020.
  • Qin Li, Sheng Lin, Changlu Liu, Yidong Liu, Fei Qiao, Yanzhi Wang, Huazhong Yang, "NS-KWS: joint optimization of near-sensor processing architecture and low-precision GRU for always-on keyword spotting", in Proc. of IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2020.
  • Xiaolong Ma, Fu-Ming Guo, Wei Niu, Xue Lin, Jian Tang, Kaisheng Ma, Bin Ren, and Yanzhi Wang, "PCONV: the missing but desirable sparsity in DNN weight pruning for real-time execution on mobile device", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, and Jieping Ye, "AutoCompress: an automatic DNN structured pruning framework for ultra-high compression rates", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin, Peiyan Dong, Yen-kuang Chen, Yuan Xie, and Yanzhi Wang, "DARB: a density-adaptive regular-block pruning for deep neural networks", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, and Bo Yuan, "Embedding compression with isotropic iterative quantization", in the Thirty Third AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, and Yanzhi Wang, "YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design", in NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving.
  • Sheng Lin, Chenghong Wang, Hongjia Li, Jieren Deng, Yanzhi Wang and Caiwen Ding, "ESMFL: Efficient and Secure Models for Federated Learning", in NeurIPS 2020 Workshop on SpicyFL (Scalability, Privacy, and Security in Federated Learning).
  • Burak Kakillioglu, Ao Ren, Yanzhi Wang, and Senem Velipasalar, "3D capsule networks for object classification with weight pruning", in IEEE Access, 2020.
  • Yidong Liu, Siting Liu, Yanzhi Wang, Fabrizio Lombardi, and Jie Han, "A Survey of Stochastic Computing Neural Networks for Machine Learning Applications", in IEEE Trans. on Neural networks and Learning Systems (TNNLS), 2020.
  • Yukui Luo, Weiqiang Liu, Yanzhi Wang, and Xiaolin Xu, "A High-Performance and Secure TRNG Based on Chaotic Cellular Automata Topology", in IEEE Trans. on Circuits and Systems I (TCAS-I), 2020.
  • Xuehai Qian, Yanzhi Wang, and Avinash Karanth, "Introduction to the Special Issue on Machine Learning Architectures and Accelerators", in IEEE Trans. on Computers, 2020.

2019

  • Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang, "Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020. (Best Paper Award)
  • Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, and Massoud Pedram, "FFT-based Deep Learning Acceleration in Distributed Computing Clusters", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020.
  • Xiaolong Ma, Sheng Lin, Shaokai Ye, Zhezhi He, Linfeng Zhang, Geng Yuan, Sia Huat Tan, Zhengang Li, Deliang Fan, Xuehai Qian, Xue Lin, Kaisheng Ma, and Yanzhi Wang,"Non-Structured DNN Weight Pruning - Is It Beneficial in Any Platform?", In IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.
  • Ruisi Zhang, Xiaoxuan Yang, Yidong Liu, Fangzhou Wang, Yanzhi Wang, Jie Han, "A Survey of Approximate Computing for Deep Neural Networks", in ACM Journal on Emerging Technologies in Computing Systems (JETC), 2019.
  • Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, and Yanzhi Wang, "Structured Weight Matrices-Based Hardware Acceleration in Deep Neural Networks", in Proc. of Great Lakes Symposium on VLSI (GLSVLSI), 2019.
  • Fuxun Yu, Zhuwei Qin, Deliang Fan, Yanzhi Wang, and Xiang Chen, "CogNet: A More Efficient and Reliable Cooperative Driving Framework for Autonomous Vehicles", in Proc. of Great Lakes Symposium on VLSI (GLSVLSI), 2019.
  • Xiaolong Ma, Zhengang Li, Hongjia Li, Ruizhe Zhao, Yanzhi Wang, and Xue Lin, "Resnet can be pruned 60x: Introducing network purification and unused path removal", in Proc. of International Conference on Computer Architecture (ISCA), 2019.
  • Sheng Lin, Xiaolong Ma, Shaokai Ye, Geng Yuan, Kaisheng Ma, and Yanzhi Wang, "Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM", in Proc. of International Conference on Computer Architecture (ISCA), 2019.
  • Yanzhi Wang and Massoud Pedram, "C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs", in Proc. of International Symposium on Field-Programmable Gate Arrays (FPGA) 2019.
  • Shengyu Fan, Yanzhi Wang, and Bo Yuan, "Configurable Hardware Architecture for Sparse-Winograd Convolutional Neural Network", in Proc. of International Symposium on Circuits and Systems (ISCAS) 2019.
  • Hongjia Li, Ning Liu, Yingyan Lin, Cong Hao, Xiaohan Chen, Deming Chen, and Yanzhi Wang, "O3BNN-R: An Out-of-Order Architecture for High-Performance Binarized Neural Network Inference with RRAM", in Proc. of International Symposium on Quality Electronic Design (ISQED) 2019.
  • Shaokai Ye, Xiaolong Ma, Sheng Lin, Zhengang Li, Xue Lin, and Yanzhi Wang, "A unified framework of DNN weight pruning and weight quantization/clustering using ADMM", in arXiv preprint arXiv:1811.01907.
  • Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang, "Tiny But Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020.
  • Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang, "Experience-driven Networking: A Deep Reinforcement Learning based Approach", in Proc. of INFOCOM 2019.
  • Geng Yuan, Xiaolong Ma, Caiwen Ding, Sheng Lin, Tianyun Zhang, Zeinab S. Jalali, Yilong Zhao, Li Jiang, Sucheta Soundarajan, and Yanzhi Wang, "An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM", in Proc. of International Symposium on Low Power Electronics and Design (ISLPED), 2019.
  • Xiaolong Ma, Sheng Lin, Shaokai Ye, Zhezhi He, Linfeng Zhang, Geng Yuan, Sia Huat Tan, Zhengang Li, Deliang Fan, Xuehai Qian, Xue Lin, Kaisheng Ma, and Yanzhi Wang, "Non-Structured DNN Weight Pruning - Is It Beneficial in Any Platform?", in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.
  • Sheng Lin, Xiaolong Ma, Shaokai Ye, Geng Yuan, Kaisheng Ma, and Yanzhi Wang, "Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM", in Proc. of International Conference on Computer Architecture (ISCA), 2019.
  • Xiaolong Ma, Zhengang Li, Hongjia Li, Ruizhe Zhao, Yanzhi Wang, and Xue Lin, "Resnet can be pruned 60x: Introducing network purification and unused path removal", in Proc. of International Conference on Computer Architecture (ISCA), 2019.
  • Yanzhi Wang, Geng Yuan, Caiwen Ding, Xiaolong Ma, Sheng Lin, Zeinab S. Jalali, Yilong Zhao, Li Jiang, Sucheta Soundarajan, and Yanzhi Wang, "An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM", in Proc. of International Symposium on Low Power Electronics and Design (ISLPED), 2019.
  • Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang, "A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers", in Proc. of European Conference on Computer Vision (ECCV), 2018.
  • Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang, "Experience-driven Networking: A Deep Reinforcement Learning based Approach", in Proc. of INFOCOM 2019.
  • Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, and Massoud Pedram, "FFT-based Deep Learning Acceleration in Distributed Computing Clusters", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020.
  • Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, and Yanzhi Wang, "Structured Weight Matrices-Based Hardware Acceleration in Deep Neural Networks", in Proc. of Great Lakes Symposium on VLSI (GLSVLSI), 2019.
  • Fuxun Yu, Zhuwei Qin, Deliang Fan, Yanzhi Wang, and Xiang Chen, "CogNet: A More Efficient and Reliable Cooperative Driving Framework for Autonomous Vehicles", in Proc. of Great Lakes Symposium on VLSI (GLSVLSI), 2019.
  • Ruisi Zhang, Xiaoxuan Yang, Yidong Liu, Fangzhou Wang, Yanzhi Wang, Jie Han, "A Survey of Approximate Computing for Deep Neural Networks", in ACM Journal on Emerging Technologies in Computing Systems (JETC), 2019.
  • Hongjia Li, Ning Liu, Yingyan Lin, Cong Hao, Xiaohan Chen, Deming Chen, and Yanzhi Wang, "O3BNN-R: An Out-of-Order Architecture for High-Performance Binarized Neural Network Inference with RRAM", in Proc. of International Symposium on Quality Electronic Design (ISQED) 2019.
  • Yanzhi Wang and Massoud Pedram, "C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs", in Proc. of International Symposium on Field-Programmable Gate Arrays (FPGA) 2019.
  • Shengyu Fan, Yanzhi Wang, and Bo Yuan, "Configurable Hardware Architecture for Sparse-Winograd Convolutional Neural Network", in Proc. of International Symposium on Circuits and Systems (ISCAS) 2019.
  • Shichao Xu, Yanzhi Wang, and Qi Zhu, "Cadence-Aware Traffic Control for Smart Cities", in Proc. of International Symposium on Quality Electronic Design (ISQED) 2019.
  • Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang, "Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020. (Best Paper Award)
  • Caiwen Ding, Hongjia Li, Weiwen Jiang, Yanzhi Wang, and Naehyuck Chang, "When ReRAM Meets NVDLA: Energy-Efficient Inter-Layer Pipelined Accelerator for Deep Neural Networks", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020.
  • Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, and Massoud Pedram, "FFT-based Deep Learning Acceleration in Distributed Computing Clusters", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020.
  • Ning Liu, Tang Liu, Yanzhi Wang, and Jieping Ye. "Adversarial Training for Deep Learning Against Limited Adversarial Examples", in Proc. of ICDM 2019.
  • Sheng Lin, Yanzhi Wang, and Wujie Wen, "Defensive-Net: Moving Target Defense against Adversarial Examples using Randomly Switched Models", in Proc. of International Symposium on Quality Electronic Design (ISQED) 2019.
  • Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang, "Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation", in Proc. of Asia and South Pacific Design Automation Conference (ASP-DAC), 2020. (Best Paper Award)
  • Xuehai Qian, Yanzhi Wang, and Avinash Karanth, "Introduction to the Special Issue on Machine Learning Architectures and Accelerators", in IEEE Trans. on Computers 2020.

2018

  • Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang, "A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers", in Proc. of European Conference on Computer Vision (ECCV), 2018.
  • Xiaolong Ma, Yanzhi Wang, and Xue Lin, "Workload-aware cache management for deep neural network accelerators", in Proc. of IEEE International Conference on Computer Design (ICCD), 2018.
  • Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, and Bo Yuan, "CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices", in Proc. of MICRO 2017.
  • Ao Ren, Zhe Li, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Ji Li, Xue海 Qian, and Bo Yuan, "SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing", in Proc. of ASPLOS 2017.
  • Zhe Li, Ao Ren, Ji Li, Qinru Qiu, Bo Yuan, Kenneth W. D. Sutherland, and Yanzhi Wang, "DSC-Net: Replicating the Performance of Deep Convolutional Neural Networks with Dynamic Stochastic Circuits", in Proc. of DAC 2017.
  • Sheng Lin, Fangxin Liu, Wujie Wen, and Yanzhi Wang, "Defensive-Net: Moving Target Defense against Adversarial Examples", in Proc. of International Symposium on Quality Electronic Design (ISQED), 2018.
  • Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang, "Experience-driven Networking: A Deep Reinforcement Learning based Approach", in Proc. of INFOCOM 2018.
  • Leng, J., A. Buyuktosunoglu, R. Bertran, P. Bose, and Yanzhi Wang, "Safe and efficient power capping for heterogeneous systems", in Proc. of MICRO 2017.
  • Wang, Y., A. R. Rahimi, N. G. Tsoutsos, R. Gupta, Yanzhi Wang, and M. Pedram, "Time-Based Computing for Energy-Efficient and Robust Neural Network Accelerators", in Proc. of DATE 2018.
  • Zhe Li, Caiwen Ding, Siyu Liao, Yanzhi Wang, and Bo Yuan, "FPGA-based acceleration of deep neural networks using block-circulant matrices", in Proc. of FPGA 2018.
  • Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang, "Experience-driven Networking: A Deep Reinforcement Learning based Approach", in Proc. of INFOCOM 2018.
  • Sheng Lin, Fangxin Liu, Wujie Wen, and Yanzhi Wang, "Defensive-Net: Moving Target Defense against Adversarial Examples", in Proc. of International Symposium on Quality Electronic Design (ISQED), 2018.
  • Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang, "Experience-driven Networking: A Deep Reinforcement Learning based Approach", in Proc. of INFOCOM 2018.
  • Leng, J., A. Buyuktosunoglu, R. Bertran, P. Bose, and Yanzhi Wang, "Safe and efficient power capping for heterogeneous systems", in Proc. of MICRO 2017.
  • Wang, Y., A. R. Rahimi, N. G. Tsoutsos, R. Gupta, Yanzhi Wang, and M. Pedram, "Time-Based Computing for Energy-Efficient and Robust Neural Network Accelerators", in Proc. of DATE 2018.
  • Zhe Li, Caiwen Ding, Siyu Liao, Yanzhi Wang, and Bo Yuan, "FPGA-based acceleration of deep neural networks using block-circulant matrices", in Proc. of FPGA 2018.
  • Siyu Liao, Zhe Li, Yanzhi Wang, Qinru Qiu, and Bo Yuan, "Energy-efficient, high-performance, and robust deep neural network using block-circulant matrices", in Proc. of DATE 2018.
  • Zhe Li, Caiwen Ding, Siyu Liao, Yanzhi Wang, and Bo Yuan, "FPGA-based acceleration of deep neural networks using block-circulant matrices", in Proc. of FPGA 2018.
  • Yanzhi Wang et al. "Efficient Deep Learning on FPGAs using Structured Compression Techniques," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018.
  • Ao Ren, Zhe Li, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Ji Li, Xuehai Qian, and Bo Yuan, "SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing", in Proc. of ASPLOS 2017.
  • Yanzhi Wang et al., "Low-Power Deep Neural Network Accelerators using Stochastic Computing and Compression Techniques," in Proc. of IEEE International Symposium on Circuits and Systems (ISCAS), 2018.
  • Z. Li, C. Ding, S. Liao, Y. Wang, and B. Yuan, "FPGA-based acceleration of deep neural networks using block-circulant matrices", in Proc. of International Symposium on Field-Programmable Gate Arrays (FPGA), 2018.
  • S. Liao, Z. Li, Y. Wang, Q. Qiu, and B. Yuan, "Energy-efficient, high-performance, and robust deep neural network using block-circulant matrices", in Proc. of Design Automation and Test in Europe (DATE), 2018.
  • Y. Wang, A. R. Rahimi, N. G. Tsoutsos, R. Gupta, Y. Wang, and M. Pedram, "Time-Based Computing for Energy-Efficient and Robust Neural Network Accelerators", in Proc. of Design Automation and Test in Europe (DATE), 2018.