Methods and apparatus for hardware-aware machine learning model training

An apparatus to train a machine learning model, the apparatus comprising:

• means for determining a hardware configuration of a target hardware platform on which the machine learning model is to be executed;
• means for assigning sparsity configurations to layers of the machine learning model based on the hardware configuration;
• and
• means for deploying the machine learning model to the target hardware platform in response to outputs of the machine learning model satisfying respective thresholds, the outputs including a quantity of clock cycles to execute the machine learning model with the layers having the assigned sparsity configurations.

A non-transitory computer readable storage medium comprising instructions that, when executed, cause a machine to at least:

• determine a hardware configuration of a target hardware platform on which a machine learning model is to be executed;
• assign sparsity configurations to layers of the machine learning model based on the hardware configuration;
• and
• deploy the machine learning model to the target hardware platform in response to outputs of the machine learning model satisfying respective thresholds, the outputs including a quantity of clock cycles to execute the machine learning model with the layers having the assigned sparsity configurations.

A method to train a machine learning model, the method comprising:

• determining a hardware configuration of a target hardware platform on which the machine learning model is to be executed;
• assigning sparsity configurations to layers of the machine learning model based on the hardware configuration;
• and
• deploying the machine learning model to the target hardware platform in response to outputs of the machine learning model satisfying respective thresholds, the outputs including a quantity of clock cycles to execute the machine learning model with the layers having the assigned sparsity configurations.