Input data transformation framework for low-voltage model

A system having a memory, computer readable instructions, and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:

• training a first machine learning model to perform input transformation for reducing low-voltage bit errors for a deep neural network operating in a low-voltage regime, wherein the training comprises:
• inputting training data into the first machine learning model such that, in response, the first machine learning model produces transformed training data;
• inputting the transformed training data into a clean machine learning model and into perturbed machine learning models, the perturbed machine learning models being generated by applying random bit errors to the clean machine learning model;
• and
• optimizing the first machine learning model based on a comparison of output of the clean machine learning model and of the perturbed machine learning models compared to groundtruth labels for the training data.

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

• training a first machine learning model to perform input transformation for reducing low-voltage bit errors for a deep neural network operating in a low-voltage regime, wherein the training comprises:
• inputting training data into the first machine learning model such that, in response, the first machine learning model produces transformed training data;
• inputting the transformed training data into a clean machine learning model and into perturbed machine learning models, the perturbed machine learning models being generated by applying random bit errors to the clean machine learning model;
• and
• optimizing the first machine learning model based on a comparison of output of the clean machine learning model and of the perturbed machine learning models compared to groundtruth labels for the training data.