The new system is parallel programming of an ionic floating-gate memory array, which allows large amounts of information to be processed simultaneously in a single operation. The research is inspired by the human brain, where neurons and synapses are connected in a dense matrix and information is processed and stored at the same location.
Sandia researchers demonstrated the ability to adjust the strength of the synaptic connections in the array using parallel computing. This will allow computers to learn and process information at the point it is sensed, rather than being transferred to the cloud for computing, greatly improving speed and efficiency and reducing the amount of power used.
Through machine learning technology, mainstream digital applications can today recognize and understand complex patterns in data. For example, popular virtual assistants, such as Amazon.com Inc.'s Alexa or Apple Inc.'s Siri, sort through large streams of data to understand voice commands and improve over time.
With the dramatic expansion of machine learning algorithms in recent years, applications are now demanding larger amounts of data storage and power to complete these difficult tasks. Traditional digital computing architecture is not designed or optimized for artificial neural networks that are the essential part of machine learning.