Scaling up the resistive memory-based in-memory computing architecture for larger and more complex neural networks may face several limitations and challenges. One major challenge is the scalability of the hardware components, as increasing the size of the neural network requires a proportional increase in the number of resistive memory cells and associated circuitry. This can lead to challenges in maintaining uniformity and consistency across a large array of memory cells, potentially impacting the overall performance and reliability of the system. Additionally, the complexity of the analog circuits and the integration of multiple layers in larger networks can introduce noise and interference, affecting the accuracy and stability of the computations. Furthermore, the power consumption and heat dissipation of the system may increase significantly with the scale, requiring efficient cooling and power management solutions to ensure optimal performance. Overall, careful design optimization, robust error correction mechanisms, and advanced calibration techniques will be essential for successfully scaling up the architecture for larger neural networks.