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Avoiding Data Inconsistency in Energy Harvesting Powered Embedded Systems.

, , , , , and . ACM Trans. Design Autom. Electr. Syst., 23 (3): 38:1-38:25 (2018)

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ENZYME: An Energy-Efficient Transient Computing Paradigm for Ultralow Self-Powered IoT Edge Devices., , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 37 (11): 2440-2450 (2018)AIM: Fast and energy-efficient AES in-memory implementation for emerging non-volatile main memory., , , , , and . DATE, page 625-628. IEEE, (2018)Modeling and Optimization for Self-powered Non-volatile IoT Edge Devices with Ultra-low Harvesting Power., , , , and . ACM Trans. Cyber Phys. Syst., 3 (3): 32:1-32:26 (2019)Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices., , , , , and . DATE, page 921-926. IEEE, (2022)An End-to-end Multi-task Object Detection using Embedded GPU in Autonomous Driving., , , , and . ISQED, page 122-128. IEEE, (2021)SAC: A Novel Multi-hop Routing Policy in Hybrid Distributed IoT System based on Multi-agent Reinforcement Learning., , , , and . ISQED, page 129-134. IEEE, (2021)Enabling Super-Fast Deep Learning on Tiny Energy-Harvesting IoT Devices., , , , , and . CoRR, (2021)Avoiding Data Inconsistency in Energy Harvesting Powered Embedded Systems., , , , , and . ACM Trans. Design Autom. Electr. Syst., 23 (3): 38:1-38:25 (2018)An Automatic and Efficient BERT Pruning for Edge AI Systems., , , , , , , and . ISQED, page 1-6. IEEE, (2022)Binary Complex Neural Network Acceleration on FPGA : (Invited Paper)., , , , , , , , , and 2 other author(s). ASAP, page 85-92. IEEE, (2021)