One embodiment sets forth a technique for generating a tree structure within a computer memory for storing sparse data. The technique includes dividing a matrix into a first plurality of equally sized regions. The technique also includes dividing at least one region in the first plurality of regions into a second plurality of regions, where the second plurality of regions includes a first region and one or more second regions that have a substantially equal number of nonzero matrix values and are formed within the first region. The technique further includes creating the tree structure within the computer memory by generating a first plurality of nodes representing the first plurality of regions, generating a second plurality of nodes representing the second plurality of regions, and grouping, under a first node representing the first region, one or more second nodes representing the one or more second regions.

A novel and useful system and method of improved power performance and lowered memory requirements for an artificial neural network based on packing memory utilizing several structured sparsity mechanisms. The invention applies to neural network (NN) processing engines adapted to implement mechanisms to search for structured sparsity in weights and activations, resulting in a considerably reduced memory usage. The sparsity guided training mechanism synthesizes and generates structured sparsity weights. A compiler mechanism within a software development kit (SDK), manipulates structured weight domain sparsity to generate a sparse set of static weights for the NN. The structured sparsity static weights are loaded into the NN after compilation and utilized by both the structured weight domain sparsity mechanism and the structured activation domain sparsity mechanism. The application of structured sparsity lowers the span of search options and creates a relatively loose coupling between the data and control planes.

A manufacturing data analyzing method and a manufacturing data analyzing device are provided. The manufacturing data analyzing method includes the following steps. Each of at least one numerical data, at least one image data and at least one text data is transformed into a vector. The vectors are gathered to obtain a combined vector. The combined vector is inputted into an inference model to obtain a defect cause and a modify suggestion.

The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.

According to an embodiment, an information processing device includes processors. The processors receive input of a plurality of pieces of input data obtained during K time periods. K is an integer equal to or greater than two. The processors estimate K first models. Each of the K first models receives input of input data and outputs output data. Each of the K first models is estimated for each period of the K time periods, using a plurality of pieces of input data obtained during the each period. The processors estimate a second model that indicates a relationship between first time parameters related to times of the K time periods, and the K first models. The processors estimate a first model corresponding to a specified second time parameter, based on the estimated second model.

Techniques for generating imputation-based, uniformly sampled parallel streams of time-series data are disclosed. A system divides into two subsets a dataset made up of multiple data streams. The data streams include interpolated data. The system trains one data correlation model using one subset of the data and applies the trained model to the other subset. The system replaces the interpolated values in the other subset with estimated values generated by the model. The system trains another data correlation model using the revised subset. The system applies the new model to the initial subset to generate estimated values for the initial subset. The system replaces the interpolated values in the initial subset with the estimated values. The system repeats the process of training data correlation models and revising previously-interpolated data points in the subsets of data until a predetermined iteration threshold is met.

One embodiment sets forth a technique for generating a tree structure within a computer memory for storing sparse data. The technique includes dividing a matrix into a first plurality of equally sized regions. The technique also includes dividing at least one region in the first plurality of regions into a second plurality of regions, where the second plurality of regions includes a first region and one or more second regions that have a substantially equal number of nonzero matrix values and are formed within the first region. The technique further includes creating the tree structure within the computer memory by generating a first plurality of nodes representing the first plurality of regions, generating a second plurality of nodes representing the second plurality of regions, and grouping, under a first node representing the first region, one or more second nodes representing the one or more second regions.

A hardware/software co-compressed computing method for a static random access memory (SRAM) computing-in-memory-based (CIM-based) processing unit includes performing a data dividing step, a sparsity step, an address assigning step and a hardware decoding and calculating step. The data dividing step is performed to divide a plurality of kernels into a plurality of weight groups. The sparsity step includes performing a weight setting step. The weight setting step is performed to set each of the weight groups to one of a zero weight group and a non-zero weight group. The address assigning step is performed to assign a plurality of index codes to a plurality of the non-zero weight groups, respectively. The hardware decoding and calculating step is performed to execute an inner product to the non-zero weight groups and the input feature data group corresponding to the non-zero weight groups to generate the output feature data group.

The present invention relates generally to a system and method for measuring the structural characteristics of an object. The object is subjected to an energy application processes and provides an objective, quantitative measurement of structural characteristics of an object. The system may include a device, for example, a percussion instrument, capable of being reproducibly placed against the object undergoing such measurement for reproducible positioning. The invention provides for a system and methods for analyzing measured characteristics utilizing machine learning to create a system for predicting pathologies from measurements.

Methods and systems for explainable machine learning are described. In an example, a processor can receive a data set from a plurality of data sources corresponding to a plurality of domains. The processor can train a machine learning model to learn a plurality of vectors that indicate impact of the plurality of domains on a plurality of assets. The machine learning model can be operable to generate forecasts relating to performance metrics of the plurality of assets based on the plurality of vectors. In some examples, the machine learning model can be a neural attention network with shared hidden layers.