Energy-efficient anomaly detection and inference on embedded systems

An apparatus, comprising:

• A memory;
• and
• at least one processor coupled to the memory, the at least one processor being configured:
• to receive an input;
• to extract, using an artificial neural network (ANN), a set of features of the input to generate a latent representation of the input;
• to generate, using the ANN, a reconstruction of the input based on the latent representation;
• to determine a reconstruction error based on the generated reconstruction and the input;
• to determine whether the input comprises in-distribution data or out-of-distribution data based on a comparison of the reconstruction error and a predefined threshold;
• and
• detect an anomaly responsive to the input being determined to comprise out-of-distribution data.

An apparatus, comprising:

• means for receiving an input;
• means for extracting, using an artificial neural network (ANN), a set of features of the input to generate a latent representation of the input;
• means for generating, using the ANN, a reconstruction of the input based on the latent representation;
• means for determining a reconstruction error based on the generated reconstruction and the input;
• means for determining whether the input comprises in-distribution data or out-of-distribution data based on a comparison of the reconstruction error and a predefined threshold;
• and
• detecting an anomaly responsive to the input being determined to comprise out-of-distribution data.

A non-transitory computer readable medium having encoded thereon program code, the program code being executed by a processor and comprising:

• program code to receive an input;
• program code to extract, using an artificial neural network (ANN), a set of features of the input to generate a latent representation of the input;
• program code to generate, using the ANN, a reconstruction of the input based on the latent representation;
• program code to determine a reconstruction error based on the generated reconstruction and the input;
• and
• program code to determine whether the input comprises in-distribution data or out-of-distribution data based on a comparison of the reconstruction error and a predefined threshold;
• and
• program code to detect an anomaly responsive to the input being determined to comprise out-of-distribution data.