Binary machine learning network with operations quantized to one bit

A device, comprising:

• one or more processors;
• and
• a memory, and wherein instructions stored in the memory cause the one or more processors to:
• receive a machine learning model, the machine learning (ML) model including a set of building blocks wherein layers of the ML model includes one or more building blocks;
• receive a set of input data;
• replicate the set of input data;
• concatenate the replicated set of input data to the set of input data to produce concatenated input data;
• normalize the concatenated input data to generate a first set of non-binary input feature values;
• and
• input the first set of non-binary input feature values to a building block of the one or more building blocks, wherein the building block is configured to:
• perform a first binary convolution operation based on the set of non-binary input feature values, comprising
• receiving the first set of non-binary input feature values;
• binarizing the set of non-binary input feature values;
• performing a first convolution operation on the binarized input feature values to generate first non-binary convolved output;
• performing a grouped convolution operation on the first non-binary convolved output;
• normalizing an output of the grouped convolution operation to generate normalized intermediate feature values;
• binarizing the normalized intermediate feature values;
• performing a second convolution operation on the binarized normalized intermediate feature values to generate convolved intermediate feature values;
• and
• outputting a first set of non-binary output features based on the convolved intermediate feature values;
• perform a non-binary convolution operation based on the first set of non-binary output features to produce a second set of non-binary input feature values;
• and
• perform a second binary convolution operation based on the second set of non-binary output features to produce a second set of non-binary output features;
• and
• output the second set of non-binary output features.

A method comprising:

• receiving a machine learning model, the machine learning (ML) model including a set of building blocks, wherein layers of the ML model includes one or more building blocks;
• receiving a set of input data;
• replicating the set of input data;
• concatenating the replicated set of input data to the set of input data to produce concatenated input data;
• normalizing the concatenated input data to generate a first set of non-binary input feature values;
• and
• inputting the first set of non-binary input feature values to a building block of the one or more building blocks, for the building block:
• performing a first binary convolution operation based on the first set of non-binary input feature values, comprising:
• receiving the first set of non-binary input feature values;
• binarizing the first set of non-binary input feature values;
• performing a first convolution operation on the binarized input feature values to generate first non-binary convolved output;
• performing a grouped convolution operation on the first non-binary convolved output;
• normalizing an output of the grouped convolution operation to generate normalized intermediate feature values;
• binarizing the normalized intermediate feature values;
• performing a second convolution operation on the binarized normalized intermediate feature values to generate convolved intermediate feature values;
• and
• outputting a first set of non-binary output features based on the convolved intermediate feature values;
• performing a non-binary convolution operation based on the first set of non-binary output features to produce a second set of non-binary input feature values;
• performing a second binary convolution operation based on the second set of non- binary input feature values to produce a second set of non-binary output features;
• and
• outputting the second set of non-binary output features.