A computer-implemented method, computer program product, and computing system is provided for providing a framework for logically representing the discretization of logic for a backtracking algorithm. In an implementation, a method may include defining a validation class representing a validation logic to be tested. A processable class may be defined representing a backtracking logic flow to be implemented. The processable class may be associated with the validation class. One or more candidate options may be evaluated based upon, at least in part, the validation logic and the backtracking logic flow.

According to some embodiments, an Arbitrary Software Logic Modeling (ASLM) data source may store electronic records associated with units, each electronic record including a unit identifier, one or more identification tags, context data, unit parameters, unit variables, and internal logic. An ASLM platform may express system requirements at a logic block level and establish the logic blocks as self-contained entities and connections in accordance with the system requirements (the established logic blocks graphically representing systems logic). The ASLM platform may then explicitly transform the systems logic automatically to output language agnostic common design information exchange model information. The ASLM platform may also translate and maintain traceability among the system requirements, common design information exchange model information, and generated code.

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

Technologies are provided for creating and using template constraint expressions in constraint-based systems. Template constraint expressions can be created that can be used to define multiple usages of a same constraint rule in a configuration model. Using the template constraint expression, the constraint rule can be translated once and used multiple times as different instances of the rule are activated. Updates to the rule can be made to the template constraint expression and applied to all of the related instances. Constraint expressions can be created based on the template constraint expression. Configuration rule definitions in a configuration model definition can be parsed to create graphical representations of the configuration model definition. One or more of the graphical representations can be used to create a template constraint expression. Multiple object instances in the configuration model can be identified that satisfy matching criteria of the template constraint expression.

In an example method, a computer system receives a query for data stored in a relational database management system. The query includes one or more first functions of a first programming language, and one or more second functions specifying computer code of a second programming language different from the first programming language. The computer system generates a logical query plan based on the query, including one or more first logical nodes corresponding to the one or more first functions, and one or more second logical nodes corresponding to the one or more second functions in an interconnected logical tree. The computer system generates a physical execution plan based on the logical query plan, and executes the physical execution plan to retrieve the data stored in the relational database management system.

An apparatus for automatic conversion of existing systems management software applications to run in multiple middleware runtime frameworks by automating the unification of runtime framework ontologies and isolating runtime dependent code in the build process of system management applications through the introduction of a runtime dependency processor and performing runtime dependency analysis.

For implementing a logic design, a method encodes a logic design as a linear array that includes a plurality of logic states. The method calculates a combination map for a state transition between a start state and an end state of the plurality of logic states using the linear array to reduce computational overhead. In addition, the method identifies undefined binary input variable transitions for the state transition on the combination map. The method resolves the undefined binary input variable transitions in the linear array. The method generates a final logic design comprising Boolean logic from the linear array with the resolved binary input variable transitions. The method implements the final logic design in hardware by generating semiconductor gates that implement the Boolean logic.

Embodiments herein describe techniques for expressing the layers of a neural network in a software model. In one embodiment, the software model includes a class that describes the various functional blocks (e.g., convolution units, max-pooling units, rectified linear units (ReLU), and scaling functions) used to execute the neural network layers. In turn, other classes in the software model can describe the operation of each of the functional blocks. In addition, the software model can include conditional logic for expressing how the data flows between the functional blocks since different layers in the neural network can process the data differently. A compiler can convert the high-level code in the software model (e.g., C++) into a hardware description language (e.g., register transfer level (RTL)) which is used to configure a hardware system to implement a neural network accelerator.

According to some embodiments, an Arbitrary Software Logic Modeling (ASLM) data source may store electronic records associated with units, each electronic record including a unit identifier, one or more identification tags, context data, unit parameters, unit variables, and internal logic. An ASLM platform may express system requirements at a logic block level and establish the logic blocks as self-contained entities and connections in accordance with the system requirements (the established logic blocks graphically representing systems logic). The ASLM platform may then explicitly transform the systems logic automatically to output language agnostic common design information exchange model information. The ASLM platform may also translate and maintain traceability among the system requirements, common design information exchange model information, and generated code.