A learning apparatus according to an embodiment includes: an input unit that inputs a set of normal data of a first system serving as a target domain and a set of normal data of a second system serving as a source domain; and a learning unit that learns a model including a first encoder having data of the target domain as an input, a second encoder having data of the source domain as an input, a discriminator having output data of either the first encoder or the second encoder as an input to discriminate whether the output data is data indicating a feature of either the target domain or the source domain, and a DeepSVDD having the output data as an input, by using the set of normal data of the first system and the set of normal data of the second system.

Network node modules within a vehicle are arranged to form a reconfigurable automotive neural network. Each network node module includes one or more subsystems for performing one or more operations and a local processing module for communicating with the one or more subsystems. A management system enables traffic from the one or more subsystems of a particular network node module to be re-routed to an external processing module upon failure of the local processing module of that particular network node module.

The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

A system, method, and a computer program product for detecting an error in artificial intelligence (AI) generated content. A neural network receives the AI generated content and at least one prompt and determines a concept in the AI generated content. Source content corresponding to the concept is retrieved. The neural network receives the source content, the concept, and a list of errors in a configuration file to determine an error in the AI generated content.

A method for operating a hardware platform for the inference calculation of a layered neural network. In the method: a first portion of input data which are required for the inference calculation of a first layer of the neural network and redundancy information relating to the input data are read in from an external working memory into an internal working memory of the computing unit; the integrity of the input data is checked based on the redundancy information; in response to the input data here being identified as error-free, the computing unit carries out at least part of the first-layer inference calculation for the input data to obtain a work result; redundancy information for the work result is determined, based which the integrity of the work result can be verified; the work result and the redundancy information are written to the external working memory.

Embodiments disclosed herein include a method of training a DNN. A processor initializes an element of an A matrix. The element may include a resistive processing unit. A processor determines incremental weight updates by updating the element with activation values and error values from a weight matrix multiplied by a chopper value. A processor reads an update voltage from the element. A processor determines a chopper product by multiplying the update voltage by the chopper value. A processor directs storage of an element of a hidden matrix. The element of the hidden matrix may include a summation of continuous iterations of the chopper product. A processor updates a corresponding element of a weight matrix based on the element of the hidden matrix reaching a threshold state.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

According to one embodiment, a magnetic reproducing processing device includes a decoder and an error correction decoder. A first signal based on a reproduced signal is input to the decoder. A signal based on a second signal output from the decoder is input to the error correction decoder. The error correction decoder is configured to correct errors in the second signal based on a check matrix. The decoder includes a neural network layer. The neural network layer includes a plurality of calculation nodes. A connection relationship between the plurality of calculation nodes is based on the check matrix.