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An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks., , , , и . COOL Chips, стр. 1-2. IEEE Computer Society, (2017)A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems., , , и . ISCAS, стр. 1790-1793. IEEE, (2015)A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications., , и . A-SSCC, стр. 237-240. IEEE, (2017)A 0.53mW ultra-low-power 3D face frontalization processor for face recognition with human-level accuracy in wearable devices., , , , и . ISCAS, стр. 1-4. IEEE, (2017)14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks., , , и . ISSCC, стр. 240-241. IEEE, (2017)An Energy-Efficient Speech-Extraction Processor for Robust User Speech Recognition in Mobile Head-Mounted Display Systems., , , и . IEEE Trans. Circuits Syst. II Express Briefs, 64-II (4): 457-461 (2017)A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application., , , и . IEEE Trans. Circuits Syst. I Regul. Pap., 66-I (5): 1794-1804 (2019)A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices., , , , и . ISCAS, стр. 1-5. IEEE, (2018)Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference., , , , , , , , , и . CHI, стр. 648:1-648:19. ACM, (2024)4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications., , , , , и . ISSCC, стр. 1-3. IEEE, (2015)