Pseudo random projection for machine learning compression

A device, comprising:

• a memory;
• and
• one or more processors configured to:
• determine a first data structure comprising pseudo random values and a second data structure comprising one or more learned values based on a target compression ratio of a first dimension associated with a first weight matrix to a second dimension;
• generate a second weight matrix comprising the second dimension with the second data structure and a seed value associated with the first data structure, the second weight matrix being generated based at least in part on the pseudo random values and the one or more learned values, and the second weight matrix being a compressed version of the first weight matrix based on the target compression ratio;
• and
• train a neural network with the second weight matrix to produce a trained machine learning model.

A non-transitory machine-readable medium comprising code that, when executed by a processor, causes the processor to perform operations comprising:

• determining a first data structure comprising pseudo random values and a second data structure comprising one or more learned values based on a target compression ratio of a first dimension associated with a first weight matrix to a second dimension;
• generating a second weight matrix comprising the second dimension with the second data structure and a seed value associated with the first data structure, the second weight matrix being generated based at least in part on the pseudo random values and the one or more learned values, and the second weight matrix being a compressed version of the first weight matrix based on the target compression ratio;
• and
• training a neural network with the second weight matrix to produce a trained machine learning model.