TY - GEN A1 - Uhlmann, Max A1 - Krysik, Milosz A1 - Wen, Jianan A1 - Frohberg, Max A1 - Baroni, Andrea A1 - Reddy, Keerthi Dorai Swamy A1 - Pérez, Eduardo A1 - Ostrovskyy, Philip A1 - Piotrowski, Krzysztof A1 - Carta, Corrado A1 - Wenger, Christian A1 - Kahmen, Gerhard T1 - A compact one-transistor-multiple-RRAM characterization platform T2 - IEEE transactions on circuits and systems I : regular papers N2 - Emerging non-volatile memories (eNVMs) such as resistive random-access memory (RRAM) offer an alternative solution compared to standard CMOS technologies for implementation of in-memory computing (IMC) units used in artificial neural network (ANN) applications. Existing measurement equipment for device characterisation and programming of such eNVMs are usually bulky and expensive. In this work, we present a compact size characterization platform for RRAM devices, including a custom programming unit IC that occupies less than 1 mm2 of silicon area. Our platform is capable of testing one-transistor-one-RRAM (1T1R) as well as one-transistor-multiple-RRAM (1TNR) cells. Thus, to the best knowledge of the authors, this is the first demonstration of an integrated programming interface for 1TNR cells. The 1T2R IMC cells were fabricated in the IHP’s 130 nm BiCMOS technology and, in combination with other parts of the platform, are able to provide more synaptic weight resolution for ANN model applications while simultaneously decreasing the energy consumption by 50 %. The platform can generate programming voltage pulses with a 3.3 mV accuracy. Using the incremental step pulse with verify algorithm (ISPVA) we achieve 5 non-overlapping resistive states per 1T1R device. Based on those 1T1R base states we measure 15 resulting state combinations in the 1T2R cells. KW - RRAM KW - Vector Matrix Multiplication Y1 - 2025 U6 - https://doi.org/10.1109/TCSI.2025.3555234 SN - 1549-8328 SP - 1 EP - 12 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER - TY - GEN A1 - Haddadi Esfahani, Ali A1 - Maye, Oliver A1 - Frohberg, Max A1 - Speh, Maria A1 - Jöbges, Michael A1 - Langendörfer, Peter T1 - Machine Learning based Real Time Detection of Freezing of Gait of Parkinson Patients Running on a Body Worn Device T2 - IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2023), 181 (2023) N2 - For those who have Parkinson's disease, one of the most incapacitating symptoms is Freezing of Gait (FOG). Gait impairment and disruptions limit everyday activities and reduce quality of daily life along with the increase in the risk of falling [1]. Thanks to recent advancement in embedded electronics and sensors as well as their adaptation in the wearable device market, low power devices are becoming more and more capable running neural networks. This enables researchers to implement complex models on wearable devices that capture and analyze sensor data to detect FOGin real-time. KW - Parkinson's disease KW - Wearable computers KW - Neural networks KW - Machine learning KW - Real-time systems Y1 - 2023 SN - 979-8-4007-0102-3 U6 - https://doi.org/10.1145/3580252.3589423 SN - 2832-2975 SP - 181 EP - 182 ER - TY - GEN A1 - Haddadi Esfahani, Ali A1 - Maye, Oliver A1 - Frohberg, Max A1 - Langendörfer, Peter T1 - Minimizing the latency of freezing of gait detection on wearable devices T2 - Vorträge : iCCC2024 - iCampµs Cottbus Conference 2024-05-14 - 2024-05-16 Cottbus Y1 - 2024 U6 - https://doi.org/10.5162/iCCC2024/2.2 SP - 49 EP - 52 PB - AMA Service GmbH CY - Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany ER - TY - GEN A1 - Frohberg, Max A1 - Weidling, Stefan A1 - Langendörfer, Peter T1 - Challenges in Developing a Wireless Sensor Network for an Agricultural Monitoring and Decision System T2 - Selected Papers from the 12th International Networking Conference, INC 2020 Y1 - 2021 SN - 978-3-030-64757-5 SN - 978-3-030-64760-5 SN - 978-3-030-64758-2 U6 - https://doi.org/10.1007/978-3-030-64758-2_16 SN - 2367-3370 SP - 224 EP - 240 PB - Springer CY - Cham ER - TY - GEN A1 - Frohberg, Max A1 - Weidling, Stefan ED - Scheffler, Thomas ED - Lange, Christoph T1 - Energy Saving Adjustments in a Wireless Sensor Network for Spatially and Temporally Highly Resolved Measurements of Environmental Parameters T2 - 19th GI/ITG KuVS Fachgespräch Sensornetze (FGSN 2022), Proceedings Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:523-16074 SP - 5 EP - 9 PB - Hochschule für Technik und Wirtschaft Berlin CY - Berlin ER - TY - GEN A1 - Esfahani, Ali Haddadi A1 - Maye, Oliver A1 - Frohberg, Max A1 - Speh, Maria A1 - Jöbges, Micheal A1 - Langendörfer, Peter T1 - Real time detection of Freezing of Gait of Parkinson patients based on machine learning running on a body worn device T2 - Procedia Computer Science Y1 - 2024 U6 - https://doi.org/10.1016/j.procs.2024.06.160 SN - 1877-0509 VL - 239 SP - 177 EP - 184 PB - Elsevier BV ER -