publications

2024

1. TMC

   ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches

   Yinqiu Liu, Hongyang Du, Dusit Niyato, and 4 more authors

   IEEE Transactions on Mobile Computing, 2024
2. WCM

   Blockchain-Empowered Lifecycle Management for AI-Generated Content Products in Edge Networks

   Yinqiu Liu, Hongyang Du, Dusit Niyato, and 5 more authors

   IEEE Wireless Communications, 2024
3. WCM

   Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

   Yinqiu Liu, Hongyang Du, Dusit Niyato, and 4 more authors

   IEEE Wireless Communications, 2024
4. COMST

   Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

   Hongyang Du*, Ruichen Zhang*, Yinqiu Liu*, and 10 more authors

   IEEE Communications Surveys & Tutorials, 2024
5. NET

   Interactive AI with Retrieval-Augmented Generation for Next Generation Networking

   Ruichen Zhang*, Hongyang Du*, Yinqiu Liu*, and 5 more authors

   IEEE Network, 2024
6. NET

   Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study

   Cong T. Nguyen, Yinqiu Liu, Hongyang Du, and 4 more authors

   IEEE Network, 2024
7. FPGA

   AutoHammer: Breaking the Compilation Wall Between Deep Neural Network and Overlay-based FPGA Accelerator

   Kai Qian, Zheng Liu, Yinqiu Liu, and 4 more authors

   In Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2024

   Abs

   Field-Programmable Gate Array (FPGA) has shown great potential in accelerating Deep Neural Networks (DNNs) due to its characteristics of programmability and high power efficiency. In address the compilation challenges between DNNs and FPGA, we propose AutoHammer, an automated compiler for mapping DNNs to different FPGAs. Specifically, AutoHammer leverages overlay techniques to enable fast and effective implementation. Moreover, three enablers are integrated into AutoHammer. First, the Model Translator optimizes the topology and predicts a DNN’s results based on different hardware configurations, built on top of a topology-based representation of DNNs. Second, the Instruction Generator generates pipeline data streams in various FPGA resource configurations by manipulating the instruction set at the upper level rapidly. Last, we realize the End-to-end Optimization, moving the whole computational processes onto the FPGA. Extensive experimental results show that AutoHammer improves great deployment efficiency when validated by 14 types of DNN models on 3 companies’ (Xilinx, Fudan Micro, and Pango Micro) mainstream FPGA chips.

2023

1. NET

   Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study

   Yinqiu Liu, Hongyang Du, Dusit Niyato, and 4 more authors

   IEEE Network, 2023
2. TII

   HPCchain: A Consortium Blockchain System Based on CPU-FPGA Hybrid-PUF for Industrial Internet of Things

   Kai Qian*, Yinqiu Liu*, Xiaoming He, and 3 more authors

   IEEE Transactions on Industrial Informatics, 2023
3. TGCN

   Fine-Grained Benchmarking and Targeted Optimization: Enabling Green IoT-Oriented Blockchain in the 6G Era

   Kai Qian*, Yinqiu Liu*, Chaoran Shu, and 2 more authors

   IEEE Transactions on Green Communications and Networking, 2023
4. CyberSci

   Proactive Caching with DRL in Cloud-Edge Assisted Cyber-Physical-Social Systems

   Yue Hu, Ning Cao, Yinqiu Liu, and 1 more author

   In 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2023
5. VTC

   Green Resource Allocation with DDPG for Knowledge Learning in Digital Twin-enabled Edges

   Xiaoming He, Yingchi Mao, Yinqiu Liu, and 3 more authors

   In 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023
6. ISCAS

   Efficient Implementation of Activation Function on FPGA for Accelerating Neural Networks

   Kai Qian, Yinqiu Liu, Zexu Zhang, and 1 more author

   In 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023
7. ICC

   microGEMM: An Effective CNN-Based Inference Acceleration for Edge Computing

   Zheng Liu, Wei Chen, Kai Qian, and 4 more authors

   In ICC 2023 - IEEE International Conference on Communications, 2023

2022

1. IoTJ

   BCmaster: A Compatible Framework for Comprehensively Analyzing and Monitoring Blockchain Systems in IoT

   Yinqiu Liu, Kai Qian, Kun Wang, and 1 more author

   IEEE Internet of Things Journal, 2022
2. ISJ

   Effective Scaling of Blockchain Beyond Consensus Innovations and Moore’s Law: Challenges and Opportunities

   Yinqiu Liu, Kai Qian, Kun Wang, and 1 more author

   IEEE Systems Journal, 2022
3. ICC

   BCadvisor: Enabling Green Blockchain Systems Through Resource-Oriented Benchmarking

   Kai Qian*, Yinqiu Liu*, Yamin Han, and 1 more author

   In ICC 2022 - IEEE International Conference on Communications, 2022

2020

1. IoTJ

   Tornado: Enabling Blockchain in Heterogeneous Internet of Things Through a Space-Structured Approach

   Yinqiu Liu, Kun Wang, Kai Qian, and 2 more authors

   IEEE Internet of Things Journal, 2020
2. WCM

   Spacechain: A Three-Dimensional Blockchain Architecture for IoT Security

   Miao Du, Kun Wang, Yinqiu Liu, and 4 more authors

   IEEE Wireless Communications, 2020

2019

1. TII

   LightChain: A Lightweight Blockchain System for Industrial Internet of Things

   Yinqiu Liu, Kun Wang, Yun Lin, and 1 more author

   IEEE Transactions on Industrial Informatics, 2019