Dimension Reduction and Principled Training on Hyperdimensional Computing Models

A method for inference classification, the method comprising:

• by a processor coupled with an associative memory:
• encoding a class vector for a class of data, the vector being determined by collecting a class of data and encoding the class of data with feature vectors and with value vectors;
• calculating a class vector by averaging each of a plurality of hypervectors within the class;
• and
• storing the class vector in the associative memory.

A system for inference classification, the system comprising:

• (i) an item memory, said item memory comprises a value memory configured to store a plurality of binary vectors representing discrete values, and a feature memory configured to store a plurality of query feature positions that are associated with the plurality of binary vectors in the value memory;
• and (ii) an associative memory configured to store a plurality of predefined class vectors;
• a circuit coupled with the value memory, the feature memory, and the associative memory, the circuit configured to:
• map a plurality of discrete values associated with a feature vector;
• stack a plurality of value vectors associated with one or more of the plurality of discrete values associated with one or more instances of binary code, such a dimension of the stack of plurality of value vectors matches the dimension of the plurality of value vectors combined with a dimension of the feature vector;
• compare a sample vector to each of a plurality of predefined class vectors by performing a matrix multiplication, the sample vector being a product of binding the feature vector and the stacked plurality of value vectors;
• identifying a respective value associated with similarity between the sample vector and each of the plurality of class vectors;
• and
• classifying the sample vector based on the maximum value associated with similarity with the plurality of predefined class vectors.

A method for inference classification, the method comprising:

• by a circuit coupled with (i) an item memory, said item memory comprises a value memory configured to store a plurality of binary vectors representing discrete values, and a feature memory configured to store a plurality of query feature positions;
• and (ii) an associative memory configured to store a plurality of predefined class vectors:
• combining the plurality of binary vectors with the plurality of query feature positions, resulting in a sample vector;
• comparing the sample vector to a plurality of class vectors of a class layer, the comparison of the sample vector to the plurality of class vectors resulting in respective comparison scores for each comparison;
• and
• outputting the comparison scores for classification as an inference label.