A Survey of Ensemble Methods for Mitigating Memristive Neural Network Non-idealities

Abstract

This a preprint and has not been peer reviewed. Data may be preliminary.

Non-Idealities in Memristor Devices and Methods of Mitigating Them

“…We summarized the four techniques in Figure 6. We evaluated the performance of ensemble methods on the handwritten digit recognition task with the MNIST dataset, with experiments gathered from 10 trials, and each ensemble method combined outputs from five different experts [21]. As a brief summary of our work, Figure 7 demonstrates the performance of the ensemble method compared to ideal and non-ideal MDNNs under device fault, endurance, retention, and endurance and retention combined non-idealities.…”

“…Figure 8 compares MDNN accuracy against the baseline and across different ensemble methods. The reader is again referred to our work in [21] for detailed implementation of these ensemble methods.…”

“…In practice, it turns out that due to the assumptions we made to derive this result that is not satisfied in reality (the assumptions being that the outputs of each network are conditionally independent and that we know the ground truth accuracies of the networks), there are other weighting schemes that can result in a greater improvement in performance depending on the task at hand; the reader is referred to our work in [21] for some more details regarding this for the specific task of handwritten digit recognition in the MNIST dataset.…”