The end of Moore’s Law is ushering in another
such upheaval, though one which is seeing a renewed interest in entirely new approaches to computing. These new approaches include quantum computing and neural-inspired computing, both which have significantly risen in popularity in recent years. Already, the first large-scale neural chips, such as IBM TrueNorth, are being produced with commercial intent, and early indications are that the technology can be entirely compatible with modern fabrication capabilities. Thus, the question is not whether industry can provide neural platforms on systems from mobile phones to supercomputers, but whether their promise will be realized. While interest in the brain as a source of inspiration goes back to early computing pioneers, it is only now that the prospect of true brain-inspired computing capabilities is really on the horizon. While approaches differ, neural-inspired computing ultimately comes down to looking to the brain for inspiration on several facets of computing: cognitive function (the ‘algorithm’), the brain’s circuitry (the ‘architecture’), and the unique properties of neurons and synapses (the ‘devices’).Recent advances in each of these domains has made brain-inspired computing more attractive, particularly in light of the growing demands for computing to analyze and interpret vast amounts of data. First, the revolution in machine learning, particularly deep learning methods, has renewed excitement in neural network algorithms and other algorithms inspired by the brain. Second, the potential broad applicability of these machine learning applications has encouraged the development of new architectures that can accelerate these functions, particularly for smartphones and other embedded platforms. Finally, the increased need for low-power devices has encouraged looking to non-silicon based modes of computation and memory storage, some of which have analog behaviors similar to those seen in neurons. While these developments provide an opportunity for the neural computing community, several challenges must be overcome if neural-inspired computing is to truly impact computing.
