Non-volatile memory with pre-trained model and inference circuit

A non-volatile storage apparatus, comprising:

• a memory controller;
• and
• a plurality of memory die assemblies connected to the memory controller, each memory die assembly of the plurality of memory die assemblies includes:
• a separate plurality of non-volatile memory cells, and
• a separate inference circuit positioned in the respective memory die assembly, the separate inference circuit is configured to use a pre-trained machine learning model with one or more metrics describing current operation of the respective memory die assembly in order to predict a defect in the respective memory die assembly prior to a non-recoverable failure in the respective memory die assembly due to the defect, the non-volatile storage apparatus is configured to perform a countermeasure prior to a non-recoverable failure in the respective memory die assembly due to the defect.

A method, comprising:

• receiving at a storage system a pre-trained model from a source external to the storage system, the storage system comprises a memory controller connected to multiple memory die assemblies;
• storing the pre-trained model in one or more blocks of non-volatile memory cells in the storage system;
• triggering a pre-failure alarm in the storage system;
• the memory controller gathering one or more metrics describing current operation of the storage system in response to the pre-failure alarm;
• the memory controller sending the one or more metrics to a first inference engine circuit on a first memory die assembly of the multiple memory die assemblies;
• the first inference engine using the pre-trained model with the one or more metrics to predict a defect in the storage system prior to a non-recoverable failure in the storage system due to the defect;
• and
• performing a countermeasure to preserve host data stored in the storage system prior to a non-recoverable failure in the storage system due to the defect.