Abstract:
A data processing system includes a user interface with a user input configured to enable a user to specify a type of simulation to be performed and at least one initial condition, where the simulation is executed using at least one sensor input from a grid structure composed of at least one of a power transmission and distribution grid. The user interface further has a display configured to visualize a representation of a result of a simulation of at least one scenario by presenting a multi-dimensional representation comprised of indicators, where each indicator corresponds to at least one simulation result. The user interface responds to a selection of one of the indicators by the user to visualize a result of the corresponding simulation. The type of simulation can be an N−k contingency analysis simulation, where k is equal to zero, 1 or greater than 1.
Abstract:
An array of resistive processing units (RPUs) comprises a plurality of rows of RPUs and a plurality of columns of RPUs wherein each RPU comprises an AND gate configured to perform an AND operation of a first stochastic bit stream received from a first stochastic translator translating a number encoded from a neuron in a row and a second stochastic bit stream received from a second stochastic translator translating a number encoded from a neuron in a column. A first storage is configured to store a weight value of the RPU, and a second storage is configured to store an amount of change to the weight value of the RPU. When the first stochastic bit stream and the second stochastic bit stream coincide, the amount of change to the weight value of the RPU is added to the weight value of the RPU.
Abstract:
An array of resistive processing units (RPUs) comprises a plurality of rows of RPUs and a plurality of columns of RPUs wherein each RPU comprises an AND gate configured to perform an AND operation of a first stochastic bit stream received from a first stochastic translator translating a number encoded from a neuron in a row and a second stochastic bit stream received from a second stochastic translator translating a number encoded from a neuron in a column. A first storage is configured to store a weight value of the RPU, and a second storage is configured to store an amount of change to the weight value of the RPU. When the first stochastic bit stream and the second stochastic bit stream coincide, the amount of change to the weight value of the RPU is added to the weight value of the RPU.
Abstract:
Embodiments relate to supporting a decision making process. The method generates a graph that represents a decision making process. The graph comprises a plurality of nodes and a plurality of edges connecting the nodes. The nodes represent local decisions contributing to a global decision of the decision making process. Each node is associated with one or more parameters used for modeling the local decision. Each edge is associated with one or more parameters used for defining a relationship between two nodes. The method simulates the graph based at least in part on the parameters of the nodes and edges to derive an output global decision of the decision making process. The method receives a change to at least one of the parameters of the graph from a user and simulates the graph based at least in part on the at least one changed parameter to determine that the output global decision changes.
Abstract:
Embodiments relate to supporting a decision making process. The method generates a graph that represents a decision making process. The graph comprises a plurality of nodes and a plurality of edges connecting the nodes. The nodes represent local decisions contributing to a global decision of the decision making process. Each node is associated with one or more parameters used for modeling the local decision. Each edge is associated with one or more parameters used for defining a relationship between two nodes. The method simulates the graph based at least in part on the parameters of the nodes and edges to derive an output global decision of the decision making process. The method receives a change to at least one of the parameters of the graph from a user and simulates the graph based at least in part on the at least one changed parameter to determine that the output global decision changes.
Abstract:
Embodiments relate to supporting a decision making process. The method generates a graph that represents a decision making process. The graph comprises a plurality of nodes and a plurality of edges connecting the nodes. The nodes represent local decisions contributing to a global decision of the decision making process. Each node is associated with one or more parameters used for modeling the local decision. Each edge is associated with one or more parameters used for defining a relationship between two nodes. The method simulates the graph based at least in part on the parameters of the nodes and edges to derive an output global decision of the decision making process. The method receives a change to at least one of the parameters of the graph from a user and simulates the graph based at least in part on the at least one changed parameter to determine that the output global decision changes.
Abstract:
Embodiments relate to supporting a decision making process. The method generates a graph that represents a decision making process. The graph comprises a plurality of nodes and a plurality of edges connecting the nodes. The nodes represent local decisions contributing to a global decision of the decision making process. Each node is associated with one or more parameters used for modeling the local decision. Each edge is associated with one or more parameters used for defining a relationship between two nodes. The method simulates the graph based at least in part on the parameters of the nodes and edges to derive an output global decision of the decision making process. The method receives a change to at least one of the parameters of the graph from a user and simulates the graph based at least in part on the at least one changed parameter to determine that the output global decision changes.
Abstract:
A data processing system includes a user interface with a user input configured to enable a user to specify a type of simulation to be performed and at least one initial condition, where the simulation is executed using at least one sensor input from a grid structure composed of at least one of a power transmission and distribution grid. The user interface further has a display configured to visualize a representation of a result of a simulation of at least one scenario by presenting a multi-dimensional representation comprised of indicators, where each indicator corresponds to at least one simulation result. The user interface responds to a selection of one of the indicators by the user to visualize a result of the corresponding simulation. The type of simulation can be an N−k contingency analysis simulation, where k is equal to zero, 1 or greater than 1.
Abstract:
A method is disclosed to simulate operation of a grid structure. The method includes specifying a type of simulation to be performed and at least one initial condition with a user interface of a device such as a mobile device, where the grid structure comprises at least one of a power generation grid and a power distribution grid. The method further includes transmitting the specified type of simulation and the at least one initial condition from the user device to a computing platform; receiving from the computing platform a result of the simulation at the user device; and visualizing the result of the simulation with the user interface. The type of simulation can be an N-k contingency analysis simulation, where k is equal to zero, 1 or greater than 1.
Abstract:
The disclosure is directed to optimizing parallel machine learning system design and performance using minibatch. A system for allocating data center resources according to embodiments includes: a machine learning process; a machine learning data set; a processing system including a P parallel processing elements for training the machine learning process using the machine learning data set, wherein the machine learning data set is split into a plurality of batches with a batch size M; and a resource manager for (1) minimizing a training time T=T(M,P) of the machine learning process over M for each value of P, and (2) efficient system design.