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001025210 0247_ $$2doi$$a10.29363/nanoge.neumatdecas.2023.013
001025210 037__ $$aFZJ-2024-02778
001025210 1001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b0$$ufzj
001025210 1112_ $$aNeuromorphic Materials, Devices, Circuits and Systems$$cValència$$d2023-01-23 - 2023-01-25$$wSpain
001025210 245__ $$aTraining-to-Learn with Memristive Devices
001025210 260__ $$c2023
001025210 3367_ $$033$$2EndNote$$aConference Paper
001025210 3367_ $$2DataCite$$aOther
001025210 3367_ $$2BibTeX$$aINPROCEEDINGS
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001025210 520__ $$aMemristive crossbar arrays are promising non-von Neumann computing technologies to enable real-world, onlinelearning in neural networks. However, their deployment to real-world learning problems is hindered by their non-linearitiesin conductance updates, variation during operation, fabrication mismatch and the realities of gradient descent training. In thiswork, we show that, with a phenomenological model of the device and bi-level optimization, it is possible to pre-train the neuralnetwork to be largely insensitive to such non-idealities on learning tasks. We demonstrate this effect using Model Agnostic Meta Learning (MAML) and a differentiable model of the conductance update on the Omniglot few-shot learning task. Since pre-training is a necessary procedure for any on-line learning scenario at the edge, our results may pave the way towards real-world applications of memristive devices without significant adaption overhead.
001025210 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001025210 588__ $$aDataset connected to CrossRef Conference
001025210 7001_ $$0P:(DE-Juel1)190500$$aYu, Zhenming$$b1$$ufzj
001025210 7001_ $$0P:(DE-Juel1)194421$$aLeroux, Nathan$$b2
001025210 773__ $$a10.29363/nanoge.neumatdecas.2023.013
001025210 8564_ $$uhttps://www.nanoge.org/proceedings/NeuMatDeCaS/636e689d63c3311959c7c0fa
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001025210 9141_ $$y2024
001025210 9201_ $$0I:(DE-Juel1)PGI-15-20210701$$kPGI-15$$lNeuromorphic Software Eco System$$x0
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