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Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China., , , , и . Remote. Sens., 5 (2): 845-861 (2013)Work-in-Progress: What to Expect of Early Training Statistics? An Investigation on Hardware-Aware Neural Architecture Search., , , , , и . CODES+ISSS, стр. 1-2. IEEE, (2022)HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search., , , и . DATE, стр. 418-421. IEEE, (2021)An Efficient Sparse LSTM Accelerator on Embedded FPGAs with Bandwidth-Oriented Pruning., , , , и . FPL, стр. 42-48. IEEE, (2023)Leaf chlorophyll content estimation from sentinel-2 MSI data., , , , , , и . IGARSS, стр. 2915-2918. IEEE, (2017)MARCO: A High-performance Task Mapping and Routing Co-optimization Framework for Point-to-Point NoC-based Heterogeneous Computing Systems., , , , , и . ACM Trans. Embed. Comput. Syst., 20 (5s): 54:1-54:21 (2021)Bringing AI to edge: From deep learning's perspective., , , , и . Neurocomputing, (2022)EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI., , , , , , , и . DATE, стр. 1-2. IEEE, (2023)EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems., , , и . ICCD, стр. 288-295. IEEE, (2020)You only search once: on lightweight differentiable architecture search for resource-constrained embedded platforms., , , , , и . DAC, стр. 475-480. ACM, (2022)