Neural Algorithms and Computing Beyond Moore’s Law


A variety of novel algorithms can be obtained by observing the neural structure of different parts of the brain.

In the April issue of the Communications of the ACM, Dr. James Aimone of Sandia National Laboratory presented an overview of how neural structures in the brain are inspiring new architectures and algorithms for electronic computing. Many of these neural structures in the brain are just starting to be understood, and are not limited to sensory neural networks that have inspired some of the recent development of deep learning. Other networks and algorithms that are now being explored include temporal neural networks, Bayesian neural algorithms, dynamic memory algorithms, cognitive inference algorithms, and self-organizing algorithms. The author suggests that future neuroscience research may continue to inspire the development of future computing paradigms that are fast, efficient, compact, and scalable.

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