@misc{SchwengerHolzingerFeyetal., author = {Schwenger, Lars and Holzinger, Philipp and Fey, Dietmar and Munoz Hernandez, Hector Gerardo and Reichenbach, Marc}, title = {EasyHBM: Simple and Fast HBM Access for FPGAs Using High-Level-Synthesis}, series = {Embedded computer systems : architectures, modeling, and simulation : 22nd International Conference, SAMOS 2022, Samos, Greece, July 3-7, 2022, proceedings}, journal = {Embedded computer systems : architectures, modeling, and simulation : 22nd International Conference, SAMOS 2022, Samos, Greece, July 3-7, 2022, proceedings}, editor = {Orailoglu, Alex and Reichenbach, Marc and Jung, Matthias}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-15073-9}, pages = {43 -- 60}, language = {en} } @misc{KnoedtelRachujReichenbach, author = {Kn{\"o}dtel, Johannes and Rachuj, Sebastian and Reichenbach, Marc}, title = {Suitability of ISAs for Data Paths Based on Redundant Number Systems: Is RISC-V the best?}, series = {25th Euromicro Conference on Digital System Design (DSD), 31 August 2022 - 02 September 2022, Maspalomas, Spain}, journal = {25th Euromicro Conference on Digital System Design (DSD), 31 August 2022 - 02 September 2022, Maspalomas, Spain}, publisher = {IEEE}, doi = {10.1109/DSD57027.2022.00041}, pages = {247 -- 253}, language = {en} } @misc{FritscherKnoedtelMallahetal., author = {Fritscher, Markus and Kn{\"o}dtel, Johannes and Mallah, Maen and Pechmann, Stefan and Perez-Bosch Quesada, Emilio and Rizzi, Tommaso and Wenger, Christian and Reichenbach, Marc}, title = {Mitigating the Effects of RRAM Process Variation on the Accuracy of Artifical Neural Networks}, series = {Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science}, journal = {Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science}, publisher = {Springer}, isbn = {978-3-031-04579-0}, issn = {0302-9743}, doi = {10.1007/978-3-031-04580-6_27}, pages = {401 -- 417}, abstract = {Weight storage is a key challenge in the efficient implementation of artificial neural networks. Novel memory technologies such as RRAM are able to greatly improve density and introduce non-volatility and multibit capabilities to this component of ANN accelerators. The usage of RRAM in this domain comes with downsides, mainly caused by cycle-to-cycle and device-to-device variability leading to erroneous readouts, greatly affecting digital systems. ANNs have the ability to compensate for this by their inherent redundancy and usually exhibit a gradual deterioration in the accuracy of the task at hand. This means, that slight error rates can be acceptable for weight storage in an ANN accelerator. In this work we link device-to-device variability to the accuracy of an ANN for such an accelerator. From this study, we can estimate how strongly a certain net is affected by a certain device parameter variability. This methodology is then used to present three mitigation strategies and to evaluate how they affect the reaction of the network to variability: a) Dropout Layers b) Fault-Aware Training c) Redundancy. These mitigations are then evaluated by their ability to improve accuracy and to lower hardware overhead by providing data for a real-word example. We improved this network's resilience in such a way that it could tolerate double the variation in one of the device parameters (standard deviation of the oxide thickness can be 0.4 nm instead of 0.2 nm while maintaining sufficient accuracy.)}, language = {en} } @misc{PechmannMaiVoelkeletal., author = {Pechmann, Stefan and Mai, Timo and V{\"o}lkel, Matthias and Mahadevaiah, Mamathamba Kalishettyhalli and Perez, Eduardo and Perez-Bosch Quesada, Emilio and Reichenbach, Marc and Wenger, Christian and Hagelauer, Amelie}, title = {A Versatile, Voltage-Pulse Based Read and Programming Circuit for Multi-Level RRAM Cells}, series = {Electronics}, volume = {10}, journal = {Electronics}, number = {5}, issn = {2079-9292}, doi = {10.3390/electronics10050530}, pages = {17}, abstract = {In this work, we present an integrated read and programming circuit for Resistive Random Access Memory (RRAM) cells. Since there are a lot of different RRAM technologies in research and the process variations of this new memory technology often spread over a wide range of electrical properties, the proposed circuit focuses on versatility in order to be adaptable to different cell properties. The circuit is suitable for both read and programming operations based on voltage pulses of flexible length and height. The implemented read method is based on evaluating the voltage drop over a measurement resistor and can distinguish up to eight different states, which are coded in binary, thereby realizing a digitization of the analog memory value. The circuit was fabricated in the 130 nm CMOS process line of IHP. The simulations were done using a physics-based, multi-level RRAM model. The measurement results prove the functionality of the read circuit and the programming system and demonstrate that the read system can distinguish up to eight different states with an overall resistance ratio of 7.9.}, language = {en} } @misc{SuawaFogouMeiselJongmannsetal., author = {Suawa Fogou, Priscile and Meisel, Tenia and Jongmanns, Marcel and H{\"u}bner, Michael and Reichenbach, Marc}, title = {Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s22093516}, pages = {17}, language = {en} } @misc{SchlipfBerkmannYamamotoetal., author = {Schlipf, Jon and Berkmann, Fritz and Yamamoto, Yuji and Reichenbach, Marc and Veleski, Mitko and Kawaguchi, Y. and M{\"o}rz, Florian and Tomm, Jens W. and Weißhaupt, David and Fischer, Inga Anita}, title = {Robust Si/Ge heterostructure metasurfaces as building blocks for wavelength-selective photodetectors}, series = {Applied Physics Letters}, volume = {122}, journal = {Applied Physics Letters}, number = {12}, issn = {1077-3118}, doi = {10.1063/5.0134458}, language = {en} } @misc{ReiserReichenbachRizzietal., author = {Reiser, Daniel and Reichenbach, Marc and Rizzi, Tommaso and Baroni, Andrea and Fritscher, Markus and Wenger, Christian and Zambelli, Cristian and Bertozzi, Davide}, title = {Technology-Aware Drift Resilience Analysis of RRAM Crossbar Array Configurations}, series = {21st IEEE Interregional NEWCAS Conference (NEWCAS), 26-28 June 2023, Edinburgh, United Kingdom}, journal = {21st IEEE Interregional NEWCAS Conference (NEWCAS), 26-28 June 2023, Edinburgh, United Kingdom}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3503-0024-6}, doi = {10.1109/NEWCAS57931.2023}, abstract = {In-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate multi-level conductance operation. The support of drift models in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they overlook key device- and array-level parameters affecting drift resilience such as the programming algorithm of RRAM cells, the choice of target conductance states and the weight-toconductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensional optimization space of drift resilience. For this purpose, a simulation framework is developed, which comes with the suitable abstractions to propagate the effects of those RRAM crossbar configuration parameters to their ultimate implications over inference performance stability.}, language = {en} } @misc{SuawaFogouHalbingerJongmannsetal., author = {Suawa Fogou, Priscile and Halbinger, Anja and Jongmanns, Marcel and Reichenbach, Marc}, title = {Noise-Robust Machine Learning Models for Predictive Maintenance Applications}, series = {IEEE Sensors Journal}, volume = {23}, journal = {IEEE Sensors Journal}, number = {13}, issn = {1558-1748}, doi = {10.1109/JSEN.2023.3273458}, pages = {15081 -- 15092}, abstract = {Predictive maintenance of equipment requires a set of data collected through sensors, from which models will learn behaviors that will allow the automatic detection or prediction of these behaviors. The objective is to anticipate unexpected situations such as sudden equipment stoppages. Industries are noisy environments due to production lines that involve a series of components. As a result, the data will always be obstructed by noise. Noise-robust predictive maintenance models, which include ensemble and deep learning models with and without data fusion, are proposed to enhance the monitoring of industrial equipment. The work reported in this article is based on two components, a milling tool, and a motor, with sound, vibration, and ultrasound data collected in real experiments. Four main tasks were performed, namely the construction of the datasets, the training of the monitoring models without adding artificial noise to the data, the evaluation of the robustness of the previously trained models by injecting several levels of noise into the test data, and the optimization of the models by a proposed noisy training approach. The results show that the models maintain their performances at over 95\% accuracy despite adding noise in the test phase. These performances decrease by only 2\% at a considerable noise level of 15-dB signal-to-noise ratio (SNR). The noisy training method proved to be an optimal solution for improving the noise robustness and accuracy of convolutional deep learning models, whose performance regression of 2\% went from a noise level of 28 to 15 dB like the other models.}, language = {en} } @misc{AssafoLautschSuawaetal., author = {Assafo, Maryam and Lautsch, Martin and Suawa, Priscile Fogou and Jongmanns, Marcel and H{\"u}bner, Michael and Reichenbach, Marc and Brockmann, Carsten and Reinhardt, Denis and Langend{\"o}rfer, Peter}, title = {The ForTune Toolbox: Building Solutions for Condition-Based and Predictive Maintenance Focusing on Retrofitting}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-6204-0}, pages = {S. 541}, language = {en} } @misc{HernandezFrickeAlKadietal., author = {Hernandez, Hector Gerardo Mu{\~n}oz and Fricke, Florian and Al Kadi, Muhammed and Reichenbach, Marc and H{\"u}bner, Michael}, title = {Edge GPU based on an FPGA Overlay Architecture using PYNQ}, series = {35th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI). 2022}, journal = {35th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI). 2022}, isbn = {978-1-6654-8128-1}, doi = {10.1109/SBCCI55532.2022.9893229}, pages = {6}, language = {en} }