Tolerating Retention Failures in Neuromorphic Fabric based on Emerging Resistive Memories

In recent years, computation is shifting from conventional high performance servers to Internet of Things (IoT) edge devices, most of which require the processing of cognitive tasks. Hence, a great effort is put in the realization of neural network (NN) edge devices and their efficiency in inferring a pretrained Neural Network. In this paper, we evaluate the retention issues of emerging resistive memories used as non-volatile weight storage for embedded NN. We exploit the asymmetric retention behavior of Spintronic based Magnetic Tunneling Junctions (MTJs), which is also present in other resistive memories like Phase-Change memory (PCM) and ReRAM, to optimize the retention of the NN accuracy over time. We propose mixed retention cell arrays and an adapted training scheme to achieve a trade-off between array size and the reliable long-term accuracy of NNs. The results of our proposed method save up to 24% of inference accuracy of an MNIST trained Multi-Layer-Perceptron on MTJ-based crossbars.