Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

Abstract: The present disclosure describes apparatuses and methods for machine learning-enabled (ML-enabled) management of storage media access. In some aspects, an ML-enabled storage controller obtains features of available blocks of storage media of a storage media system. The controller can receive, from a host system, a request to write data and determine features of the data to be written to the storage media. The controller provides the respective features of the available blocks and the data to a neural network and receives, from the neural network, a selected block of the available blocks for writing of the data. The selected block may include an ML-optimized selection from the available blocks based on the features of both the available blocks and the data. The controller then writes the data of the request to the ML-selected block of storage media of the storage media system, which may improve storage media performance.

Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.