DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.013
Publication date: 9th January 2023
Memristive crossbar arrays are promising non-von Neumann computing technologies to enable real-world, online
learning in neural networks. However, their deployment to real-world learning problems is hindered by their non-linearities
in conductance updates, variation during operation, fabrication mismatch and the realities of gradient descent training. In this
work, we show that, with a phenomenological model of the device and bi-level optimization, it is possible to pre-train the neural
network to be largely insensitive to such non-idealities on learning tasks. We demonstrate this effect using Model Agnostic Meta Learning (MAML) and a differentiable model of the conductance update on the Omniglot few-shot learning task. Since pre-training is a necessary procedure for any on-line learning scenario at the edge, our results may pave the way towards real-world applications of memristive devices without significant adaption overhead.
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