  Deep learning has made remarkable progress in various tasks, surpassing human
performance in some cases. However, one drawback of neural networks is
catastrophic forgetting, where a network trained on one task forgets the
solution when learning a new one. To address this issue, recent works have
proposed solutions based on Binarized Neural Networks (BNNs) incorporating
metaplasticity. In this work, we extend this solution to quantized neural
networks (QNNs) and present a memristor-based hardware solution for
implementing metaplasticity during both inference and training. We propose a
hardware architecture that integrates quantized weights in memristor devices
programmed in an analog multi-level fashion with a digital processing unit for
high-precision metaplastic storage. We validated our approach using a combined
software framework and memristor based crossbar array for in-memory computing
fabricated in 130 nm CMOS technology. Our experimental results show that a
two-layer perceptron achieves 97% and 86% accuracy on consecutive training of
MNIST and Fashion-MNIST, equal to software baseline. This result demonstrates
immunity to catastrophic forgetting and the resilience to analog device
imperfections of the proposed solution. Moreover, our architecture is
compatible with the memristor limited endurance and has a 15x reduction in
memory
