Nanoelectronic neurocomputing: Status and prospects

Potential advantages of specialized hardware for neuromorphic computing had been recognized several decades ago (see, e.g., Refs. [1, 2]), but the need for it became especially acute recently, due to significant advances of the computational neuroscience and machine learning. The most vivid example is given by the deep learning in convolution neuromorphic networks [3]: the recent dramatic progress of this technology, with it's rapid extension to several important applications, was enabled by the use of modern GPU clusters [4, 5]. Even higher performance and lower power consumption has been recently demonstrated using FPGAS [5-7] and custom digital circuits [5, 8].