CLASSIFYING FEATURES USING A NEUROSYNAPTIC SYSTEM

摘要

Embodiments of the invention provide a method comprising receiving a set of features extracted from input data, training a linear classifier based on the set of features extracted, and generating a first matrix using the linear classifier. The first matrix includes multiple dimensions. Each dimension includes multiple elements. Elements of a first dimension correspond to the set of features extracted. Elements of a second dimension correspond to a set of classification labels. The elements of the second dimension are arranged based on one or more synaptic weight arrangements. Each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels. The neurosynaptic core circuit is programmed with synaptic connectivity information based on the synaptic weight arrangements. The core circuit is configured to classify one or more objects of interest in the input data

主权项

1. A method, comprising:
receiving a set of features extracted from input data; training a linear classifier based on the set of features extracted; generating a first matrix using the linear classifier, wherein the first matrix includes multiple dimensions, wherein each dimension includes multiple elements, wherein elements of a first dimension correspond to the set of features extracted, and wherein elements of a second dimension correspond to a set of classification labels; arranging the elements of the second dimension based on one or more synaptic weight arrangements, wherein each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels; and programming a neurosynaptic core circuit with synaptic connectivity information based on the one or more synaptic weight arrangements, wherein the core circuit is configured to classify one or more objects of interest in the input data.