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Tiny compensation of pressure drift measurements due to long exposures to high temperatures.

, , , , , and . I2MTC, page 1-5. IEEE, (2023)

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Low-complexity Machine Learning Architecture for Hardware-aware True Random Number Generators Assessment and Continuous Monitoring., , , , and . PRIME, page 221-224. IEEE, (2023)Tiny compensation of pressure drift measurements due to long exposures to high temperatures., , , , , and . I2MTC, page 1-5. IEEE, (2023)Monitoring Hardware True Random Number Generators with Artificial Neural Networks: Problem Modeling and Training Dataset Generation., , , , , , and . ApplePies, volume 1110 of Lecture Notes in Electrical Engineering, page 291-296. Springer, (2023)Low-Power Anomaly Detection and Classification System based on a Partially Binarized Autoencoder for In-Sensor Computing., , , , , and . ICECS, page 1-5. IEEE, (2021)Ultra-Tiny Neural Network for Compensation of Post-soldering Thermal Drift in MEMS Pressure Sensors., , , , , , , and . ISCAS, page 1-5. IEEE, (2023)Dynamically Adaptive Accumulator for in-sensor ANN Hardware Accelerators., , , , , and . ISCAS, page 1-5. IEEE, (2024)Quantized ID-CNN for a Low-power PDM-to-PCM Conversion in TinyML KWS Applications., , , , , , and . AICAS, page 154-157. IEEE, (2022)