Neuromorphic computing gets ready for the (really) big time

By D. Monroe. 2014

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “The progress of EPANNs will crucially depend on implementations that take advantage of the increased computational power of parallel computation with GPUs and neuromorphic hardware (Jo et al., 2010; Monroe, 2014).”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “With neuromorphic chips, which implement neural structures at the hardware level, expected much more widely in the coming years [278], the continuation of deep learning and the longevity of its success can be highly anticipated, ensuring the opportunity for sustained progress in NLP.”
A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, background): “sed attention due to the approaching end of Moore’s law, the increasing power demands associated with Dennard scaling, and the low bandwidth between CPU and memory known as the von Neumann bottleneck [2]. Neuromorphic computers have the potential to perform complex calculations faster, more power-ef”