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Binary-Stochasticity-Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better-than-Software Accuracy., , , , , , , , , и 6 other автор(ы). Adv. Intell. Syst., (января 2024)NimbleAI: Towards Neuromorphic Sensing-Processing 3D-integrated Chips., , , , , , , , , и 39 other автор(ы). DATE, стр. 1-6. IEEE, (2023)End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays., , , , , , , , и . VLSI-SoC, стр. 1-5. IEEE, (2022)Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators., , , , , , , , и . IRPS, стр. 3. IEEE, (2022)In-memory neural network accelerator based on phase change memory (PCM) with one-selector/one-resistor (1S1R) structure operated in the subthreshold regime., , , , , , и . IMW, стр. 1-4. IEEE, (2023)Enhancing reliability of a strong physical unclonable function (PUF) solution based on virgin-state phase change memory (PCM)., , , , , , , , , и . IRPS, стр. 1-6. IEEE, (2023)Mitigating read-program variation and IR drop by circuit architecture in RRAM-based neural network accelerators., , и . IRPS, стр. 3. IEEE, (2022)