BTW, a very informative article I just stumbled upon.
In the future, memristor appears to be a promising candidate for high-density, high-energy efficiency, ultra-fast, low-latency, low power, large-capacity non-volatile memory. Therefore, many companies (Samsung, Panasonic, HP, Micron, Sony, Yangtze Memory Technologies Co., Ltd. (YMTC), Crossbar etc.) are engaged in research and development of memristors.
These memristor arrays can build more integrated neural network structures, including artificial neural networks (ANN) convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), and spike neural networks (SNNs).
The second article which is actually referencing an old thesis, however focuses on design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs).
So to extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components.
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can ...
www.ncbi.nlm.nih.gov
Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current ...
www.frontiersin.org