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DL is based on computational models that are, to a certain extent, bio\u2010inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond\u2010complementary metal\u2013oxide\u2013semiconductor (CMOS) technology\u2014memristors\u2014as a potential solution for the implementation of power\u2010efficient in\u2010memory computing, DL accelerators, and spiking neural networks. 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