  Memristors offer significant advantages as in-memory computing devices due to
their non-volatility, low power consumption, and history-dependent
conductivity. These attributes are particularly valuable in the realm of
neuromorphic circuits for neural networks, which currently face limitations
imposed by the Von Neumann architecture and high energy demands. This study
evaluates the feasibility of using memristors for in-memory processing by
constructing and training three digital convolutional neural networks with the
datasets MNIST, CIFAR10 and CIFAR100. Subsequent conversion of these networks
into memristive systems was performed using Memtorch. The simulations,
conducted under ideal conditions, revealed minimal precision losses of nearly
1% during inference. Additionally, the study analyzed the impact of tile size
and memristor-specific non-idealities on performance, highlighting the
practical implications of integrating memristors in neuromorphic computing
systems. This exploration into memristive neural network applications
underscores the potential of Memtorch in advancing neuromorphic architectures.
