Fujitsu Announces Better Memory Capacity for Deep Neural Learning Networks

Deep neural learning (DNL) technologies have become an advanced tool for computers to identify the content of images, decipher audio recordings, and analyze other complex inputs.  A DNL network consists of thousands of layers of nodes. Each node processes individual content from the input and generates a few interpretations. These interpretations are sent to nodes in a subsequent layer for further processing. This continues through several layers. After an input has been processed through the network, the output is compared to a desired output and the computer generates an error reading. This error is fed back through the network, so that the each interpretation by a single node can be weighted. Based on the error, some interpretations are more heavily considered for the final output. There may be thousands of…


Link to Full Article: Fujitsu Announces Better Memory Capacity for Deep Neural Learning Networks