Reconfigurable, hyperdimensional neural network architecture

A method for processing information comprising:

• receiving input information;
• extracting at least one hyperdimensional (HD) vector representing at least one feature from the input information using a neural network, where the neural network comprises no more than one multiply and accumulate operator at each layer of the neural network;
• and
• classifying the at least one HD vector using a classifier, where the classifier does not comprise any multiply and accumulate operators.

An apparatus comprising at least one processor and at least one non-transient computer readable media, where the at least one non-transient computer readable media stores instructions that, when executed by the at least one processor, causes the apparatus to perform operations comprising:

• receiving input information;
• extracting at least one hyperdimensional (HD) vector representing at least one feature from the input information using a neural network, where the neural network comprises no more than one multiply and accumulate operator at each layer of the first neural network;
• and
• classifying the at least one HD vector using a classifier to produce an indicium of classification for the at least one HD vector, where the classifier does not comprise any multiply and accumulate operators.