Abstract: Devices and/or methods may monitor data objects stored on a storage medium to detect presence of data objects that have not been accessed for a threshold period of time. Responsive to detecting that a data object has not been accessed for that threshold period of time, devices and methods may determine whether to perform an operation on the data object. The operation may include one or more of deleting the data object, purging the data object, converting the data object, compressing the data object, or transferring the data object to another storage device.

Abstract: Systems, methods, and devices having systems-on-a-chip (SOCs) may utilize bootup code stored external from the SOCs. The bootup code may be verified by the SOCs. If the bootup code is not verified within a selected duration, the SOC may be reset or disabled. If the bootup code is verified within the selected duration, a reset circuit may be disabled.

Abstract: Systems and methods are disclosed for distributed decentralized machine learning model training. In certain embodiments, a first node in a network may comprise a circuit configured to receive an initial machine learning model having an initial parameter set, apply the local data to update parameters of the initial machine learning model to generate an updated machine learning model, transmit a copy of the updated machine learning model from the first node to a plurality of neighboring nodes in the network via a network interface, receive, via the network interface, a modified machine learning model from a first neighboring node, the modified machine learning model having parameters set based on local data of the first neighboring node, modify the updated machine learning model based on the modified machine learning model, and apply the updated machine learning model to control operations at the first node.