TY - GEN A1 - Fritscher, Markus A1 - Singh, Simranjeet A1 - Rizzi, Tommaso A1 - Baroni, Andrea A1 - Reiser, Daniel A1 - Mallah, Maen A1 - Hartmann, David A1 - Bende, Ankit A1 - Kempen, Tim A1 - Uhlmann, Max A1 - Kahmen, Gerhard A1 - Fey, Dietmar A1 - Rana, Vikas A1 - Menzel, Stephan A1 - Reichenbach, Marc A1 - Krstic, Milos A1 - Merchant, Farhad A1 - Wenger, Christian T1 - A flexible and fast digital twin for RRAM systems applied for training resilient neural networks T2 - Scientific Reports N2 - 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). KW - RRAM KW - Neural network KW - digital twin Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-73439-z SN - 2045-2322 VL - 14 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Fritscher, Markus A1 - Uhlmann, Max A1 - Ostrovskyy, Philip A1 - Reiser, Daniel A1 - Chen, Junchao A1 - Schubert, Andreas A1 - Schulze, Carsten A1 - Kahmen, Gerhard A1 - Fey, Dietmar A1 - Reichenbach, Marc A1 - Krstic, Milos A1 - Wenger, Christian T1 - Area-efficient digital design using RRAM-CMOS standard cells T2 - 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) N2 - 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. KW - RRAM Y1 - 2024 SN - 979-8-3503-5411-9 SN - 979-8-3503-5412-6 U6 - https://doi.org/10.1109/ISVLSI61997.2024.00026 SN - 2159-3477 VL - 18 SP - 81 EP - 87 PB - IEEE ER - TY - GEN A1 - Uhlmann, Max A1 - Pérez-Bosch Quesada, Emilio A1 - Fritscher, Markus A1 - Pérez, Eduardo A1 - Schubert, Markus Andreas A1 - Reichenbach, Marc A1 - Ostrovskyy, Philip A1 - Wenger, Christian A1 - Kahmen, Gerhard T1 - One-Transistor-Multiple-RRAM Cells for Energy-Efficient In-Memory Computing T2 - 21st IEEE Interregional NEWCAS Conference (NEWCAS) N2 - 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. KW - RRAM KW - In-Memory Computing Y1 - 2023 SN - 979-8-3503-0024-6 SN - 979-8-3503-0025-3 U6 - https://doi.org/10.1109/NEWCAS57931.2023.10198073 SN - 2474-9672 SN - 2472-467X PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Fritscher, Markus A1 - Uhlmann, Max A1 - Ostrovskyy, Philip A1 - Reiser, Daniel A1 - Chen, Junchao A1 - Wen, Jianan A1 - Schulze, Carsten A1 - Kahmen, Gerhard A1 - Fey, Dietmar A1 - Reichenbach, Marc A1 - Krstic, Milos A1 - Wenger, Christian ED - Wenger, Christian T1 - RISC-V CPU design using RRAM-CMOS standard cells T2 - IEEE transactions on very large scale integration (VLSI) systems N2 - 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. KW - RRAM KW - Logic gates Y1 - 2025 UR - https://ieeexplore.ieee.org/document/10960690 U6 - https://doi.org/10.1109/TVLSI.2025.3554476 SN - 1063-8210 SP - 1 EP - 9 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - New York ER -