@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{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{FrickeScharobaRachujetal., author = {Fricke, Florian and Scharoba, Stefan and Rachuj, Sebastian and Konopik, Andreas and Kluge, Florian and Hofstetter, Georg and Reichenbach, Marc}, title = {Application Runtime Estimation for AURIX Embedded MCU Using Deep Learning }, 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}, doi = {10.1007/978-3-031-15074-6_15}, pages = {235 -- 249}, language = {en} } @misc{MahmoodScharobaSchorlemeretal., author = {Mahmood, Safdar and Scharoba, Stefan and Schorlemer, Jonas and Schulz, Christian and H{\"u}bner, Michael and Reichenbach, Marc}, title = {Detecting Improvised Land-mines using Deep Neural Networks on GPR Image Dataset targeting FPGAs }, series = {IEEE Nordic Circuits and Systems Conference (NORCAS), 25-26 October 2022, Oslo, Norway}, journal = {IEEE Nordic Circuits and Systems Conference (NORCAS), 25-26 October 2022, Oslo, Norway}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3503-4550-6}, doi = {10.1109/norcas57515.2022.9934735}, pages = {1 -- 7}, language = {en} } @misc{MahmoodHuebnerReichenbach, author = {Mahmood, Safdar and H{\"u}bner, Michael and Reichenbach, Marc}, title = {A Design-Space Exploration Framework for Application-Specific Machine Learning Targeting Reconfigurable Computing}, series = {Applied Reconfigurable Computing. Architectures, Tools, and Applications, 19th International Symposium, ARC 2023, Cottbus, Germany, September 27-29, 2023}, journal = {Applied Reconfigurable Computing. Architectures, Tools, and Applications, 19th International Symposium, ARC 2023, Cottbus, Germany, September 27-29, 2023}, editor = {Palumbo, Francesca and Keramidas, Georgios and Voros, Nikolaos and Diniz, Pedro C.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-42921-7}, doi = {10.1007/978-3-031-42921-7_27}, pages = {371 -- 374}, language = {en} } @misc{FritscherUhlmannOstrovskyyetal., author = {Fritscher, Markus and Uhlmann, Max and Ostrovskyy, Philip and Reiser, Daniel and Chen, Junchao and Schubert, Andreas and Schulze, Carsten and Kahmen, Gerhard and Fey, Dietmar and Reichenbach, Marc and Krstic, Milos and Wenger, Christian}, title = {Area-efficient digital design using RRAM-CMOS standard cells}, series = {2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)}, volume = {18}, journal = {2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)}, publisher = {IEEE}, isbn = {979-8-3503-5411-9}, issn = {2159-3477}, doi = {10.1109/ISVLSI61997.2024.00026}, pages = {81 -- 87}, abstract = {Extending the scalability of digital integrated circuits through novel device concepts is an attractive option. Among these concepts, resistive random access memory (RRAM) devices allow fast and nonvolatile operation. However, building large memristive systems is still challenging since large analog circuits have to be designed and integrated. In this paper, we propose a novel solution - the implementation of digital standard cells by the means of RRAM devices. While this methodology is universal, with applications ranging from few-device-circuits to large macroblocks, we demonstrate it for a 2T2R-cell. The benefits of using RRAM devices are demonstrated by implementing a NAND standard cell merely consuming the area of two transistors. This cell is about 25 \% smaller than the equivalent CMOS NAND in the same technology. We use these cells to implement a half adder, beating the area of the equivalent CMOS implementation using more sophisticates gates by 15 \%. Lastly, we fully integrate this novel standard cell into a digital standard cell library and perform a synthesis and layout of a RISC-V CPU core.}, 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{ShahinRottaArchilaetal., author = {Shahin, Keyvan and Rotta, Randolf and Archila, Oscar and Mykytyn, Pavlo and Nattke, Matthias and Reichenbach, Marc and Nolte, J{\"o}rg and Natarov, Roman}, title = {A Modular Communication Architecture for Adaptive UAV Swarms}, series = {2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)}, journal = {2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS)}, publisher = {IEEE}, address = {Berlin}, isbn = {979-8-3503-4647-3}, doi = {10.1109/COINS57856.2023.10189245}, pages = {6}, abstract = {Small Unmanned Aerial Vehicles (UAVs) have vast application potential from industrial logistics and disaster monitoring to smart farming, for example, to create maps with Normalized Difference Vegetation Index (NDVI) cameras and AI-based image classification or the precision spot application of fertilizers. Operating multiple UAVs in parallel, including those with different specializations, enables efficient coverage of large areas. While existing research focused mainly on predefined flight formations for swarms or autonomous missions for single UAVs, the focus of this work is on multiple UAVs with individually adaptable missions. We derive communication system requirements from swarm coordination algorithms and present a respective design. The resulting modular architecture enables future research on autonomous adaptive UAV swarms and their use as flying sensor platforms.}, language = {en} } @misc{UhlmannPerezBoschQuesadaFritscheretal., author = {Uhlmann, Max and P{\´e}rez-Bosch Quesada, Emilio and Fritscher, Markus and P{\´e}rez, Eduardo and Schubert, Markus Andreas and Reichenbach, Marc and Ostrovskyy, Philip and Wenger, Christian and Kahmen, Gerhard}, title = {One-Transistor-Multiple-RRAM Cells for Energy-Efficient In-Memory Computing}, series = {21st IEEE Interregional NEWCAS Conference (NEWCAS)}, journal = {21st IEEE Interregional NEWCAS Conference (NEWCAS)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, isbn = {979-8-3503-0024-6}, issn = {2474-9672}, doi = {10.1109/NEWCAS57931.2023.10198073}, pages = {5}, abstract = {The use of resistive random-access memory (RRAM) for in-memory computing (IMC) architectures has significantly improved the energy-efficiency of artificial neural networks (ANN) over the past years. Current RRAM-technologies are physically limited to a defined unambiguously distinguishable number of stable states and a maximum resistive value and are compatible with present complementary metal-oxide semiconductor (CMOS)-technologies. In this work, we improved the accuracy of current ANN models by using increased weight resolutions of memristive devices, combining two or more in-series RRAM cells, integrated in the back end of line (BEOL) of the CMOS process. Based on system level simulations, 1T2R devices were fabricated in IHP's 130nm SiGe:BiCMOS technology node, demonstrating an increased number of states. We achieved an increase in weight resolution from 3 bit in ITIR cells to 6.5 bit in our 1T2R cell. The experimental data of 1T2R devices gives indications for the performance and energy-efficiency improvement in ITNR arrays for ANN applications.}, language = {en} } @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{KnoedtelMunozHernandezLehnertetal., author = {Kn{\"o}dtel, Johannes and Munoz-Hernandez, Hector Gerardo and Lehnert, Alexander and Thieu, Gia Bao and Gesper, Sven and Pay{\´a}-Vay{\´a}, Guillermo and Reichenbach, Marc}, title = {TAPRE-HBM: Trace-Based Processor Rapid Emulation Using HBM on FPGAs}, series = {Applied Reconfigurable Computing. Architectures, Tools, and Applications, 19th International Symposium, ARC 2023, Cottbus, Germany, September 27-29, 2023, Proceedings}, journal = {Applied Reconfigurable Computing. Architectures, Tools, and Applications, 19th International Symposium, ARC 2023, Cottbus, Germany, September 27-29, 2023, Proceedings}, editor = {Palumbo, Francesca and Keramidas, Georgios and Voros, Nikolaos and Diniz, Pedro C.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-42920-0}, doi = {10.1007/978-3-031-42921-7_21}, pages = {307 -- 321}, 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{DoeScharobaReichenbachetal., author = {Doe, Nina and Scharoba, Stefan and Reichenbach, Marc and Herglotz, Christian}, title = {Comparison of bluetooth low energy (BLE), Wi-Fi, serial and 5G in IoMT}, series = {Proceedings of the 18th international joint conference on biomedical engineering systems and technologies (BIOSTEC 2025), Porto, Portugal : Volume 2}, volume = {2: HEALTHINF}, journal = {Proceedings of the 18th international joint conference on biomedical engineering systems and technologies (BIOSTEC 2025), Porto, Portugal : Volume 2}, publisher = {Scitepress - Science And Technology Publications Lda.}, address = {Set{\´u}bal, Portugal}, isbn = {978-989-758-731-3; ISSN: 2184-4305}, doi = {10.5220/0013321500003911}, pages = {859 -- 866}, abstract = {With the inception of Industry 4.0, incorporating technologies like the Internet of Things (IoT) into healthcare has become essential. This integration is commonly referred to as the Internet of Medical Things (IoMT). The IoMT is the connection of medical devices using wired or wireless data transmission technology to allow data exchange with the goal of improving the overall healthcare delivery. Despite the numerous advantages that IoMT brings into the healthcare process, there are potential performance challenges that may occur if factors such as data quality and reliability of the IoT devices in different environmental settings are not properly considered. The purpose of this paper is to analyse the performance of connected medical IoT devices that are used for heartrate monitoring based on the aforementioned factors. The setup of the IoMT consists of sensor nodes, which transmit the Electrocardiogram (ECG) data through a multi-protocol gateway to a central server for further data processing. This paper presents the performance analysis of the comparison of four communication technologies: Serial (UART), Bluetooth Low Energy (BLE), Wi-Fi, and 5G NR for real-time ECG monitoring applications, while taking notice of environmental factors that may affect performance. The sensor data transmission is evaluated based on round trip time (RTT) latency, ensuring a desirable throughput and minimal or no data loss. The data readings were taken at varying distances (0.1m to 17m) and sampling rates (300Hz and 1000Hz). The experimental results show that while Serial communication achieves the lowest latency (3.96ms - 4.37ms), Wi-Fi demonstrates consistent Gateway-Server performance (40ms - 60msRTT), 5G excels in short-range communication (1.8ms - 2.0ms Sensor Node-Gateway RTT), and BLE providesbalanced performance (4.86ms - 7.57ms latency). Wi-Fi performed better in long-range scenarios (43.48ms -66.23ms RTT) and maintaining stable performance at longer ranges while 5G shows superior performance in short-range, high-frequency scenarios.}, language = {en} }