Abstract: Apparatuses, systems, and techniques to predict a probability of an error in processing units, such as those of a data center. In at least one embodiment, the probability of an error occurring in a processing unit is identified using a machine learning model trained using one or more previously trained machine learning models, in which the machine learning model is smaller than the previously trained machine learning models.

Abstract: Apparatuses, systems, and techniques to predict a probability of an error or anomay in processing units, such as those of a data center. In at least one embodiment, the probability of an error occuring in a proccessing unit is identified using multiple trained machine learning models, in which the trained machine learning models each outputs, for example, the probability of an error occuring within a different predetermined time period.

Abstract: A system and method may determine if a class of process (e.g. NN execution, cryptocurrency mining, graphic processing) is executing on a processor, or which class is executing, by calculating or determining features from execution telemetry or measurements collected from processors executing processes, and determining from at least a subset of the features the likelihood that the processor is executing the class of process. Execution telemetry may include data regarding or describing the execution of the process, or describing hardware used to execute the process, such as processor temperature, memory usage, etc.

Abstract: A system measures performance activity of a user using a motion capture device, such as one or more knee sleeves each a plurality of inertial-measurement unit. Captured motion characteristic data is classified against previously captured data and then mapped to particular audio effects, which are then altered in different ways depending on the classification and provided the user. The audio feedback works to improve the user's performance of the activity by changing between negative and positive feedback depending on the user's performance. The system regularly measures the motion capture data, altering audio effects provided to the user, until the user reaches an improvement goal.