@misc{FritscherSinghRizzietal., author = {Fritscher, Markus and Singh, Simranjeet and Rizzi, Tommaso and Baroni, Andrea and Reiser, Daniel and Mallah, Maen and Hartmann, David and Bende, Ankit and Kempen, Tim and Uhlmann, Max and Kahmen, Gerhard and Fey, Dietmar and Rana, Vikas and Menzel, Stephan and Reichenbach, Marc and Krstic, Milos and Merchant, Farhad and Wenger, Christian}, title = {A flexible and fast digital twin for RRAM systems applied for training resilient neural networks}, series = {Scientific Reports}, volume = {14}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2045-2322}, doi = {10.1038/s41598-024-73439-z}, pages = {13}, abstract = {Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the "ideal memristor" which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a "push button" modeling framework that allows to evaluate the efficacy of the device at system level during early device design stages. Speed, accuracy, and adaptability are the fundamental requirements of this modelling framework. In this paper, we propose a digital twin (DT)-like modeling framework that automatically creates RRAM device models from device measurement data. Furthermore, the model incorporates the peripheral circuit to ensure accurate energy and performance evaluations. We demonstrate the DT generation and DT usage for multiple RRAM technologies and applications and illustrate the achieved performance of our GPU implementation. We conclude with the application of our modeling approach to measurement data from two distinct fabricated devices, validating its effectiveness in a neural network processing an Electrocardiogram (ECG) dataset and incorporating Fault Aware Training (FAT).}, 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{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{FritscherUhlmannOstrovskyyetal., author = {Fritscher, Markus and Uhlmann, Max and Ostrovskyy, Philip and Reiser, Daniel and Chen, Junchao and Wen, Jianan and Schulze, Carsten and Kahmen, Gerhard and Fey, Dietmar and Reichenbach, Marc and Krstic, Milos and Wenger, Christian}, title = {RISC-V CPU design using RRAM-CMOS standard cells}, series = {IEEE transactions on very large scale integration (VLSI) systems}, journal = {IEEE transactions on very large scale integration (VLSI) systems}, editor = {Wenger, Christian}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {New York}, issn = {1063-8210}, doi = {10.1109/TVLSI.2025.3554476}, pages = {1 -- 9}, abstract = {The breakdown of Dennard scaling has been the driver for many innovations such as multicore CPUs and has fueled the research into novel devices such as resistive random access memory (RRAM). These devices might be a means to extend the scalability of integrated circuits since they allow for fast and nonvolatile operation. Unfortunately, large analog circuits need to be designed and integrated in order to benefit from these cells, hindering the implementation of large systems. This work elaborates on a novel solution, namely, creating digital standard cells utilizing RRAM devices. Albeit this approach can be used both for small gates and large macroblocks, we illustrate it for a 2T2R-cell. Since RRAM devices can be vertically stacked with transistors, this enables us to construct a nand standard cell, which merely consumes the area of two transistors. This leads to a 25\% area reduction compared to an equivalent CMOS nand gate. We illustrate achievable area savings with a half-adder circuit and integrate this novel cell into a digital standard cell library. A synthesized RISC-V core using RRAM-based cells results in a 10.7\% smaller area than the equivalent design using standard CMOS gates.}, language = {en} }