ReARTSim: an ReRAM ARray Transient Simulator with GPU optimized runtime acceleration

Abstract

The demand for computation driven by machine learning and deep learning applications has experienced exponential growth over the past five years (Sevilla et al 2022 2022 International Joint Conference on Neural Networks (IJCNN) (IEEE) pp 1-8), leading to a significant surge in computing hardware products. Meanwhile, this rapid increase has exacerbated the memory wall bottleneck within mainstream Von Neumann architectures (Hennessy and Patterson et al 2011 Computer architecture: a quantitative approach (Elsevier)). For instance, NVIDIA graphical processing units (GPUs) have gained nearly a 200x increase in fp32 computing power, transitioning from P100 to H100 in the last five years (NVIDIA Tesla P100 2023 ( https://www.nvidia.com/en-us/data-center/tesla-p100/ ); NVIDIA H100 Tensor Core GPU 2023 ( https://www.nvidia.com/en-us/data-center/h100/ )), accompanied by a mere 8x scaling in memory bandwidth. Addressing the need to mitigate data movement challenges, process-in-memory designs, especially resistive random-access memory (ReRAM)-based solutions, have emerged as compelling candidates (Verma et al 2019 IEEE Solid-State Circuits Mag. 11 43–55; Sze et al 2017 Proc. IEEE 105 2295–329). However, this shift in hardware design poses distinct challenges at the design phase, given the limitations of existing hardware design tools. Popular design tools today can be used to characterize analog behavior via SPICE tools (PrimeSim HSPICE 2023 ( https://www.synopsys.com/implementation-and-signoff/ams-simulation/primesim-hspice.html )), system and logical behavior using Verilog tools (VCS 2023 ( https://www.synopsys.com/verification/simulation/vcs.html )), and mixed signal behavior through toolbox like CPPSIM (Meninger 2023 ( https://www.cppsim.org/Tutorials/wideband_fracn_tutorial.pdf )). Nonetheless, the design of in-memory computing systems, especially those involving non-CMOS devices, presents a unique need for characterizing mixed-signal computing behavior across a large number of cells within a memory bank. This requirement falls beyond the scope of conventional design tools. In this paper, we bridge this gap by introducing the ReARTSim framework—a GPU-accelerated mixed-signal transient simulator for analyzing ReRAM crossbar array. This tool facilitates the characterization of analog circuit and device behavior on a large scale, while also providing enhanced simulation performance for complex algorithm analysis, sign-off, and verification.

Keywords

• ReRAM
• in memory computing
• transient simulation
• GPU acceleration
• AI simulation