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Feature Article

Feature Article

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.

The video overview of the article is available here

The complete article is available here

Technology Spotlight

Technology Spotlight

Generating Stochastic Bits using Tunable Quantum Systems

Nanoscale Quantum Dots can generate time series of truly random bits for stochastic computing.

The International Nanodevices and Computing Conference (INC 2019) was recently held in Grenoble France, 2-5 April 2019. This was co-sponsored by IRDS, IEEE Rebooting Computing, and the European SiNANO Institute. See here for the conference program.

Several of the talks from the INC 2019 were recorded and are available on IEEE.tv.

These included a talk by Prof. Erik Blair of Baylor University, Waco Texas. Dr. Blair spoke about stochastic computing, which requires a time series of uncorrelated random bits. While stochastic bits can be generated using a classical pseudo-random number generator, he proposed using the quantum properties of a two-level quantum system comprising two quantum dots on the nanometer scale. These quantum dots can be made lithographically, or alternatively the quantum properties of certain molecules can be used. Dr. Blair proposed that this nanoscale Quantum Stochastic Number Generator could be integrated with nanoscale CMOS or Quantum Cellular Automata circuits to implement a nanoscale stochastic computer.

The video presentation by Prof. Blair is available here