A minimization method for minimizing the size of a file is provided, the minimization method including preparing an absolute value length that is equal to or larger than the largest data length among the data lengths of the data of a plurally of coordinates, and a plurality of relative value lengths equal to or less than the absolute value length, and identifying a relative value length causing minimization of the size among a plurality of relative value lengths.

A minimization method for minimizing the size of a file is provided, the minimization method including preparing an absolute value length that is equal to or larger than the largest data length among the data lengths of the data of a plurally of coordinates, and a plurality of relative value lengths equal to or less than the absolute value length, and identifying a relative value length causing minimization of the size among a plurality of relative value lengths.

Aspects of the disclosure relate to a dynamic event securitization and neural network analysis system. A dynamic event inspection and securitization computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may securitize event data prior to authorizing execution of the event. A neural network event analysis computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may utilize a plurality of event analysis modules, a neural network, and a decision engine to analyze the risk level values of data sharing events. The dynamic event inspection and securitization computing platform may interface with the neural network event analysis computing platform by generating data securitization flags that may be utilized by the neural network event analysis computing platform to modify event analysis results generated by the event analysis modules.

Aspects of the disclosure relate to a dynamic event securitization and neural network analysis system. A dynamic event inspection and securitization computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may securitize event data prior to authorizing execution of the event. A neural network event analysis computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may utilize a plurality of event analysis modules, a neural network, and a decision engine to analyze the risk level values of data sharing events. The dynamic event inspection and securitization computing platform may interface with the neural network event analysis computing platform by generating data securitization flags that may be utilized by the neural network event analysis computing platform to modify event analysis results generated by the event analysis modules.

A processor includes a scalar processor core and a vector coprocessor core coupled to the scalar processor core. The scalar processor core is configured to retrieve an instruction stream from program storage, and pass vector instructions in the instruction stream to the vector coprocessor core. The vector coprocessor core includes a register file, a plurality of execution units, and a table lookup unit. The register file includes a plurality of registers. The execution units are arranged in parallel to process a plurality of data values. The execution units are coupled to the register file. The table lookup unit is coupled to the register file in parallel with the execution units. The table lookup unit is configured to retrieve table values from one or more lookup tables stored in memory by executing table lookup vector instructions in a table lookup loop.

A processor includes a scalar processor core and a vector coprocessor core coupled to the scalar processor core. The scalar processor core is configured to retrieve an instruction stream from program storage, and pass vector instructions in the instruction stream to the vector coprocessor core. The vector coprocessor core includes a register file, a plurality of execution units, and a table lookup unit. The register file includes a plurality of registers. The execution units are arranged in parallel to process a plurality of data values. The execution units are coupled to the register file. The table lookup unit is coupled to the register file in parallel with the execution units. The table lookup unit is configured to retrieve table values from one or more lookup tables stored in memory by executing table lookup vector instructions in a table lookup loop.

A processor having an instruction set including a load-store instruction having operands specifying, from amongst the registers in at least one register file, a respective destination of each of two load operations, a respective source of a store operation, and a pair of address registers arranged to hold three memory addresses, the three memory addresses being a respective load address for each of the two load operations and a respective store address for the store operation. The load-store instruction further includes three stride operands each specifying a respective stride value for each of the two load addresses and one store address, wherein at least some possible values of each stride operand specify the respective stride value by specifying one of a plurality of fields within a stride register in one of the one or more register files, each field holding a different stride value.

A digital signal processor having at least one streaming address generator, each with dedicated hardware, for generating addresses for writing multi-dimensional streaming data that comprises a plurality of elements. Each at least one streaming address generator is configured to generate a plurality of offsets to address the streaming data, and each of the plurality of offsets corresponds to a respective one of the plurality of elements. The address of each of the plurality of elements is the respective one of the plurality of offsets combined with a base address.

Disclosed herein are an apparatus for analyzing non-informative firmware and a method using the apparatus. The method includes detecting a target instruction for firmware analysis in a memory map in non-informative firmware, generating an analysis list based on memory map information corresponding to the target instruction, and generating a visualized analysis result corresponding to the firmware by grouping the entries of the analysis list by preset reference bytes.

Disclosed herein are an apparatus for analyzing non-informative firmware and a method using the apparatus. The method includes detecting a target instruction for firmware analysis in a memory map in non-informative firmware, generating an analysis list based on memory map information corresponding to the target instruction, and generating a visualized analysis result corresponding to the firmware by grouping the entries of the analysis list by preset reference bytes.