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2020 – today
- 2024
[j13]Yongan Zhang
, Xiaofan Zhang, Pengfei Xu, Yang Zhao, Cong Hao, Deming Chen, Yingyan Lin:
AutoAI2C: An Automated Hardware Generator for DNN Acceleration on Both FPGA and ASIC. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(10): 3143-3156 (2024)- [j12]Nan Wu, Yingjie Li, Hang Yang, Hanqiu Chen, Steve Dai, Cong Hao, Cunxi Yu, Yuan Xie:
Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and Prospect. ACM Trans. Design Autom. Electr. Syst. 29(4): 1-42 (2024) - [j11]Jennifer Hasler, Cong Hao:
Programmable Analog System Benchmarks Leading to Efficient Analog Computation Synthesis. ACM Trans. Reconfigurable Technol. Syst. 17(1): 12:1-12:25 (2024) - [c68]Hanqiu Chen, Yitu Wang, Luis Vitório Cargnini, Mohammadreza Soltaniyeh, Dongyang Li, Gongjin Sun, Pradeep Subedi, Andrew Chang, Yiran Chen, Cong Hao:
ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using Gaussian Mixture Model. DAC 2024: 106:1-106:6 - [c67]Rishov Sarkar, Rachel Paul, Cong Hao:
LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization. FCCM 2024: 104-114 - [c66]Rishov Sarkar, Cong Hao:
Ph.D. Project: Modernizing High-Level Hardware Design Workflows. FCCM 2024: 245-246 - [c65]Dongning Ma, Cong Hao, Xun Jiao:
Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process. ISQED 2024: 1-5 - [c64]Stefan Abi-Karam, Rishov Sarkar, Allison Seigler, Sean Lowe, Zhigang Wei, Hanqiu Chen, Nanditha Rao, Lizy Kurian John, Aman Arora, Cong Hao:
HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond. MLCAD 2024: 23:1-23:9 - [c63]Jingtian Dang, Jianming Tong, Anupam Golder, Cong Hao, Arijit Raychowdhury, Tushar Krishna:
Accurate Low-Degree Polynomial Approximation of Non-Polynomial Operators for Fast Private Inference in Homomorphic Encryption. MLSys 2024 - [i47]Rishov Sarkar, Rachel Paul, Cong Hao:
LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization. CoRR abs/2404.09471 (2024) - [i46]Stefan Abi-Karam, Rishov Sarkar, Allison Seigler, Sean Lowe, Zhigang Wei, Hanqiu Chen, Nanditha Rao, Lizy Kurian John, Aman Arora, Cong Hao:
HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond. CoRR abs/2405.00820 (2024) - [i45]Yuchen Xia, Jiho Kim, Yuhan Chen, Haojie Ye, Souvik Kundu, Cong Hao, Nishil Talati:
Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. CoRR abs/2408.04693 (2024) - [i44]Hanqiu Chen, Yitu Wang, Luis Vitório Cargnini, Mohammadreza Soltaniyeh, Dongyang Li, Gongjin Sun, Pradeep Subedi, Andrew Chang, Yiran Chen, Cong Hao:
ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using Gaussian Mixture Model. CoRR abs/2408.05614 (2024) - [i43]Hanqiu Chen, Xuebin Yao, Pradeep Subedi, Cong Hao:
Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation. CoRR abs/2408.05617 (2024) - [i42]Cong Hao:
Exploring and Exploiting Runtime Reconfigurable Floating Point Precision in Scientific Computing: a Case Study for Solving PDEs. CoRR abs/2409.15073 (2024) - [i41]Yaoyao Long, Zhenming Liu, Cong Hao, Farrokh Ayazi:
MEMS Gyroscope Multi-Feature Calibration Using Machine Learning Technique. CoRR abs/2410.07519 (2024) - 2023
- [j10]Stefan Abi-Karam, Cong Hao:
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization. Softw. Pract. Exp. 53(11): 212-218 (2023) - [j9]Nan Wu, Yuan Xie, Cong Hao:
IronMan-Pro: Multiobjective Design Space Exploration in HLS via Reinforcement Learning and Graph Neural Network-Based Modeling. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(3): 900-913 (2023) - [c62]Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu, Zhiwen Fan, Wuyang Chen, Zhangyang Wang, Shiyu Chang, Sijia Liu, Cong Hao:
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices. ASP-DAC 2023: 745-750 - [c61]Nan Wu, Yingjie Li, Cong Hao, Steve Dai, Cunxi Yu, Yuan Xie:
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks. DAC 2023: 1-6 - [c60]Akshay Karkal Kamath, Stefan Abi-Karam, Ashwin Bhat, Cong Hao:
M5: Multi-modal Multi-task Model Mapping on Multi-FPGA with Accelerator Configuration Search. DATE 2023: 1-6 - [c59]Rishov Sarkar, Cong Hao:
LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis. FCCM 2023: 1-11 - [c58]Hanqiu Chen, Cong Hao:
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference. FCCM 2023: 195-201 - [c57]Hanqiu Chen, Cong Hao:
Hardware/Software Co-design for Machine Learning Accelerators. FCCM 2023: 233-235 - [c56]Rishov Sarkar, Cong Hao:
From Acceleration to Accelerating Acceleration: Modernizing the Accelerator Landscape using High-Level Synthesis. FCCM 2023: 236-238 - [c55]Stefan Abi-Karam, Cong Hao:
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization. FPL 2023: 212-218 - [c54]Rishov Sarkar, Stefan Abi-Karam, Yuqi He, Lakshmi Sathidevi, Cong Hao:
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference. HPCA 2023: 1099-1112 - [c53]Stefan Abi-Karam, Rishov Sarkar, Dejia Xu, Zhiwen Fan, Zhangyang Wang, Cong Hao:
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing. ICCAD 2023: 1-9 - [c52]Hanqiu Chen, Hang Yang, Stephen B. R. Fitzmeyer, Cong Hao:
Rapid-INR: Storage Efficient CPU-Free DNN Training Using Implicit Neural Representation. ICCAD 2023: 1-9 - [c51]Rishov Sarkar, Hanxue Liang, Zhiwen Fan, Zhangyang Wang, Cong Hao:
Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-Level Sparsity via Mixture-of-Experts. ICCAD 2023: 1-9 - [c50]Yuhong Li, Jiajie Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen:
Extensible and Efficient Proxy for Neural Architecture Search. ICCV 2023: 6176-6187 - [c49]Lakshmi Sathidevi, Abhinav Sharma, Nan Wu, Xun Jiao, Cong Hao:
PreAxC: Error Distribution Prediction for Approximate Computing Quality Control using Graph Neural Networks. ISQED 2023: 1-7 - [i40]Nan Wu, Yingjie Li, Cong Hao, Steve Dai, Cunxi Yu, Yuan Xie:
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks. CoRR abs/2303.08256 (2023) - [i39]Stefan Abi-Karam, Cong Hao:
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization. CoRR abs/2303.16459 (2023) - [i38]Hanqiu Chen, Cong Hao:
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference. CoRR abs/2304.06831 (2023) - [i37]Rishov Sarkar, Cong Hao:
LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis. CoRR abs/2304.11219 (2023) - [i36]Rishov Sarkar, Hanxue Liang, Zhiwen Fan, Zhangyang Wang, Cong Hao:
Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-level Sparsity via Mixture-of-Experts. CoRR abs/2305.18691 (2023) - [i35]Hanqiu Chen, Hang Yang, Stephen B. R. Fitzmeyer, Cong Hao:
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation. CoRR abs/2306.16699 (2023) - [i34]Stefan Abi-Karam, Rishov Sarkar, Dejia Xu, Zhiwen Fan, Zhangyang Wang, Cong Hao:
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing. CoRR abs/2308.05930 (2023) - 2022
- [c48]Zishen Wan, Ashwin Sanjay Lele, Bo Yu, Shaoshan Liu, Yu Wang, Vijay Janapa Reddi, Cong Hao, Arijit Raychowdhury:
Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities. AICAS 2022: 291-295 - [c47]Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao:
LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models. ASAP 2022: 11-18 - [c46]Hanqiu Chen, Cong Hao:
Mask-Net: A Hardware-efficient Object Detection Network with Masked Region Proposals. ASAP 2022: 131-138 - [c45]Nan Wu, Hang Yang, Yuan Xie, Pan Li, Cong Hao:
High-level synthesis performance prediction using GNNs: benchmarking, modeling, and advancing. DAC 2022: 49-54 - [c44]Xinyi Zhang, Cong Hao, Peipei Zhou, Alex K. Jones, Jingtong Hu:
H2H: heterogeneous model to heterogeneous system mapping with computation and communication awareness. DAC 2022: 601-606 - [c43]Hanchen Ye, Cong Hao, Jianyi Cheng, Hyunmin Jeong, Jack Huang, Stephen Neuendorffer, Deming Chen:
ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation. HPCA 2022: 741-755 - [c42]Nan Wu, Yuan Xie, Cong Hao:
AI-assisted Synthesis in Next Generation EDA: Promises, Challenges, and Prospects. ICCD 2022: 207-214 - [c41]Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao:
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU. IISWC 2022: 130-145 - [c40]Qing Lu, Xiaowei Xu, Shunjie Dong, Cong Hao, Lei Yang, Cheng Zhuo, Yiyu Shi:
RT-DNAS: Real-Time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation. MICCAI (5) 2022: 602-612 - [c39]Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li:
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation. NeurIPS 2022 - [c38]Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang:
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design. NeurIPS 2022 - [i33]Nan Wu, Hang Yang, Yuan Xie, Pan Li, Cong Hao:
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing. CoRR abs/2201.06848 (2022) - [i32]Nan Wu, Jiwon Lee, Yuan Xie, Cong Hao:
Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information. CoRR abs/2201.08455 (2022) - [i31]Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi, Zihang Qiao, Cong Hao:
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration. CoRR abs/2201.08475 (2022) - [i30]Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya, Michael Pellauer, Angshuman Parashar, Sivasankaran Rajamanickam, Cong Hao, Tushar Krishna:
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity. CoRR abs/2201.08916 (2022) - [i29]Rishov Sarkar, Stefan Abi-Karam, Yuqi He, Lakshmi Sathidevi, Cong Hao:
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming. CoRR abs/2204.13103 (2022) - [i28]Xinyi Zhang, Cong Hao, Peipei Zhou, Alex K. Jones, Jingtong Hu:
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness. CoRR abs/2204.13852 (2022) - [i27]Zishen Wan, Ashwin Sanjay Lele, Bo Yu, Shaoshan Liu, Yu Wang, Vijay Janapa Reddi, Cong Hao, Arijit Raychowdhury:
Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities. CoRR abs/2205.07149 (2022) - [i26]Xiaofan Zhang, Yao Chen, Cong Hao, Sitao Huang, Yuhong Li, Deming Chen:
Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems. CoRR abs/2206.03326 (2022) - [i25]Qing Lu, Xiaowei Xu, Shunjie Dong, Cong Hao, Lei Yang, Cheng Zhuo, Yiyu Shi:
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation. CoRR abs/2206.04682 (2022) - [i24]Haoyu Wang, Nan Wu, Hang Yang, Cong Hao, Pan Li:
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation. CoRR abs/2207.05984 (2022) - [i23]Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao:
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU. CoRR abs/2210.03900 (2022) - [i22]Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu, Zhiwen Fan, Wuyang Chen, Zhangyang Wang, Shiyu Chang, Sijia Liu, Cong Hao:
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices. CoRR abs/2210.08578 (2022) - [i21]Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang:
M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design. CoRR abs/2210.14793 (2022) - 2021
- [j8]Cong Hao, Jordan Dotzel, Jinjun Xiong, Luca Benini, Zhiru Zhang, Deming Chen:
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Codesign. IEEE Des. Test 38(4): 7-26 (2021) - [j7]Jianwei Zheng, Chao Lu, Cong Hao, Deming Chen, Donghui Guo:
Improving the Generalization Ability of Deep Neural Networks for Cross-Domain Visual Recognition. IEEE Trans. Cogn. Dev. Syst. 13(3): 607-620 (2021) - [j6]Cheng Gong, Yao Chen, Ye Lu, Tao Li, Cong Hao, Deming Chen:
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization. IEEE Trans. Computers 70(5): 696-710 (2021) - [c37]Cong Hao, Deming Chen:
Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems. AICAS 2021: 1-5 - [c36]Xinheng Liu, Yao Chen, Cong Hao, Ashutosh Dhar, Deming Chen:
WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs. ASAP 2021: 258-265 - [c35]Lixiang Li, Yao Chen, Zacharie Zirnheld, Pan Li, Cong Hao:
MELOPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank. DAC 2021: 601-606 - [c34]Dongning Ma, Rahul Thapa, Xingjian Wang, Xun Jiao, Cong Hao:
Workload-Aware Approximate Computing Configuration. DATE 2021: 920-925 - [c33]Nan Wu, Yuan Xie, Cong Hao:
IRONMAN: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning. ACM Great Lakes Symposium on VLSI 2021: 39-44 - [c32]Yao Chen, Cole Hawkins, Kaiqi Zhang, Zheng Zhang, Cong Hao:
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low Bitwidth Quantization, and Ultra-Low Latency Acceleration. ACM Great Lakes Symposium on VLSI 2021: 157-162 - [c31]Chaojian Li, Zhongzhi Yu, Yonggan Fu, Yongan Zhang, Yang Zhao, Haoran You, Qixuan Yu, Yue Wang, Cong Hao, Yingyan Lin:
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark. ICLR 2021 - [c30]Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville:
Adversarial Graph Augmentation to Improve Graph Contrastive Learning. NeurIPS 2021: 15920-15933 - [c29]Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen:
Generic Neural Architecture Search via Regression. NeurIPS 2021: 20476-20490 - [i20]Nan Wu, Yuan Xie, Cong Hao:
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning. CoRR abs/2102.08138 (2021) - [i19]Cong Hao, Jordan Dotzel, Jinjun Xiong, Luca Benini, Zhiru Zhang, Deming Chen:
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design. CoRR abs/2103.15750 (2021) - [i18]Kaiqi Zhang, Cole Hawkins, Xiyuan Zhang, Cong Hao, Zheng Zhang:
On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method. CoRR abs/2104.03420 (2021) - [i17]Cong Hao, Deming Chen:
Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems. CoRR abs/2104.04000 (2021) - [i16]Lixiang Li, Yao Chen, Zacharie Zirnheld, Pan Li, Cong Hao:
MELOPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank. CoRR abs/2104.09616 (2021) - [i15]Yao Chen, Cole Hawkins, Kaiqi Zhang, Zheng Zhang, Cong Hao:
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration. CoRR abs/2105.06250 (2021) - [i14]Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville:
Adversarial Graph Augmentation to Improve Graph Contrastive Learning. CoRR abs/2106.05819 (2021) - [i13]Xinheng Liu, Yao Chen, Cong Hao, Ashutosh Dhar, Deming Chen:
WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs. CoRR abs/2107.04244 (2021) - [i12]Hanchen Ye, Cong Hao, Jianyi Cheng, Hyunmin Jeong, Jack Huang, Stephen Neuendorffer, Deming Chen:
ScaleHLS: Scalable High-Level Synthesis through MLIR. CoRR abs/2107.11673 (2021) - [i11]Yuhong Li, Cong Hao, Pan Li, Jinjun Xiong, Deming Chen:
Generic Neural Architecture Search via Regression. CoRR abs/2108.01899 (2021) - [i10]Nan Wu, Huake He, Yuan Xie, Pan Li, Cong Hao:
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation. CoRR abs/2109.06265 (2021) - 2020
- [c28]Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, Jinjun Xiong, Wen-mei W. Hwu, Deming Chen:
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions. DAC 2020: 1-6 - [c27]Pengfei Xu, Xiaofan Zhang, Cong Hao, Yang Zhao, Yongan Zhang, Yue Wang, Chaojian Li, Zetong Guan, Deming Chen, Yingyan Lin:
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs. FPGA 2020: 40-50 - [c26]Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, Jinjun Xiong, Wen-Mei Hwu, Deming Chen:
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices. ACM Great Lakes Symposium on VLSI 2020: 283-290 - [c25]Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas S. Huang, Honghui Shi, Wen-Mei Hwu, Deming Chen:
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems. MLSys 2020 - [i9]Pengfei Xu, Xiaofan Zhang, Cong Hao, Yang Zhao, Yongan Zhang, Yue Wang, Chaojian Li, Zetong Guan, Deming Chen, Yingyan Lin:
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs. CoRR abs/2001.03535 (2020) - [i8]Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, Jinjun Xiong, Wen-Mei W. Hwu, Deming Chen:
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions. CoRR abs/2005.02563 (2020) - [i7]Cheng Gong, Yao Chen, Ye Lu, Tao Li, Cong Hao, Deming Chen:
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization. CoRR abs/2005.08501 (2020) - [i6]Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, Jinjun Xiong, Wen-Mei Hwu, Deming Chen:
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices. CoRR abs/2010.07185 (2020)
2010 – 2019
- 2019
- [c24]Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, Jinjun Xiong, Kyle Rupnow, Wen-Mei Hwu, Deming Chen:
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge. DAC 2019: 206 - [c23]Yao Chen, Jiong He, Xiaofan Zhang, Cong Hao, Deming Chen:
Cloud-DNN: An Open Framework for Mapping DNN Models to Cloud FPGAs. FPGA 2019: 73-82 - [c22]Cong Hao, Yao Chen, Xinheng Liu, Atif Sarwari, Daryl Sew, Ashutosh Dhar, Bryan Wu, Dongdong Fu, Jinjun Xiong, Wen-Mei Hwu, Junli Gu, Deming Chen:
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving. ICCAD 2019: 1-8 - [c21]Cheng Gong, Tao Li, Ye Lu, Cong Hao, Xiaofan Zhang, Deming Chen, Yao Chen:
µL2Q: An Ultra-Low Loss Quantization Method for DNN Compression. IJCNN 2019: 1-8 - [c20]Yao Chen, Kai Zhang, Cheng Gong, Cong Hao, Xiaofan Zhang, Tao Li, Deming Chen:
T-DLA: An Open-source Deep Learning Accelerator for Ternarized DNN Models on Embedded FPGA. ISVLSI 2019: 13-18 - [c19]Cong Hao, Junli Gu, Deming Chen, Atif Sarwari, Zhijie Jin, Husam Abu-Haimed, Daryl Sew, Yuhong Li, Xinheng Liu, Bryan Wu, Dongdong Fu:
A Hybrid GPU + FPGA System Design for Autonomous Driving Cars. SiPS 2019: 121-126 - [i5]Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, Jinjun Xiong, Kyle Rupnow, Wen-Mei Hwu, Deming Chen:
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge. CoRR abs/1904.04421 (2019) - [i4]Xiaofan Zhang, Cong Hao, Yuhong Li, Yao Chen, Jinjun Xiong, Wen-Mei W. Hwu, Deming Chen:
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices. CoRR abs/1905.08369 (2019) - [i3]Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, Jinjun Xiong, Thomas S. Huang, Honghui Shi, Wen-Mei Hwu, Deming Chen:
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection. CoRR abs/1906.10327 (2019) - [i2]Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas S. Huang, Honghui Shi, Wen-mei W. Hwu, Deming Chen:
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems. CoRR abs/1909.09709 (2019) - [i1]Cong Hao, Yao Chen, Xinheng Liu, Atif Sarwari, Daryl Sew, Ashutosh Dhar, Bryan Wu, Dongdong Fu, Jinjun Xiong, Wen-Mei Hwu, Junli Gu, Deming Chen:
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving. CoRR abs/1911.07446 (2019) - 2018
- [c18]Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, Luka Cehovin Zajc, Tomás Vojír, Goutam Bhat, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernández, Álvaro García-Martín, Álvaro Iglesias-Arias, A. Aydin Alatan, Abel González-García, Alfredo Petrosino, Alireza Memarmoghadam, Andrea Vedaldi, Andrej Muhic, Anfeng He, Arnold W. M. Smeulders, Asanka G. Perera, Bo Li, Boyu Chen, Changick Kim, Changsheng Xu, Changzhen Xiong, Cheng Tian, Chong Luo, Chong Sun, Cong Hao, Daijin Kim, Deepak Mishra, Deming Chen, Dong Wang, Dongyoon Wee, Efstratios Gavves, Erhan Gundogdu, Erik Velasco-Salido, Fahad Shahbaz Khan, Fan Yang, Fei Zhao, Feng Li, Francesco Battistone, George De Ath, Gorthi R. K. Sai Subrahmanyam, Guilherme Sousa Bastos, Haibin Ling, Hamed Kiani Galoogahi, Hankyeol Lee, Haojie Li, Haojie Zhao, Heng Fan, Honggang Zhang, Horst Possegger, Houqiang Li, Huchuan Lu, Hui Zhi, Huiyun Li, Hyemin Lee, Hyung Jin Chang, Isabela Drummond, Jack Valmadre, Jaime Spencer Martin, Javaan Singh Chahl, Jin Young Choi, Jing Li, Jinqiao Wang, Jinqing Qi, Jinyoung Sung, Joakim Johnander, João F. Henriques, Jongwon Choi, Joost van de Weijer, Jorge Rodríguez Herranz, José M. Martínez, Josef Kittler, Junfei Zhuang, Junyu Gao, Klemen Grm, Lichao Zhang, Lijun Wang, Lingxiao Yang, Litu Rout, Liu Si, Luca Bertinetto, Lutao Chu, Manqiang Che, Mario Edoardo Maresca, Martin Danelljan, Ming-Hsuan Yang, Mohamed H. Abdelpakey, Mohamed S. Shehata, Myunggu Kang, Namhoon Lee, Ning Wang, Ondrej Miksik, Payman Moallem, Pablo Vicente-Moñivar, Pedro Senna, Peixia Li, Philip H. S. Torr, Priya Mariam Raju, Ruihe Qian, Qiang Wang, Qin Zhou, Qing Guo, Rafael Martin Nieto, Rama Krishna Sai Subrahmanyam Gorthi, Ran Tao, Richard Bowden, Richard M. Everson, Runling Wang, Sangdoo Yun, Seokeon Choi, Sergio Vivas, Shuai Bai, Shuangping Huang, Sihang Wu, Simon Hadfield, Siwen Wang, Stuart