System and methods for efficiently implementing a convolutional neural network incorporating binarized filter and convolution operation for performing image classification

A method comprising:

• inputting data representing an image into a convolutional neural network (CNN), the CNN including a plurality of convolutional layers, a set of filters or weights for at least one of the convolutional layers, and a set of input data to the at least one of the convolutional layers;
• performing a convolution operation between the set of input data and the set of filters or weights by determining a product of a scaling factor and a binary representation of the set of input data convolved with the set of filters or weights;
• and
• applying an operation to an output of a last of the plurality of convolutional layers.

A system comprising:

• a set of processing blocks, wherein the set of processing blocks includes a normalization block, an activation block, a convolution block, and a pooling block, with image input data being processed in that order;
• a computer-implemented process for representing the convolution block, wherein the computer-implemented process represents a convolution operation between a set of data and a set of filters or weights by a product of a scaling factor and a binary representation of the set of filters or weights convolved with the set of data;
• and
• a classifier applied to an output of the pooling block.