6 results about "Fixed point algorithm" patented technology

The invention provides an automatic security vulnerability detection method and system for multiple types of scripts in an embedded system, electronic equipment and a readable storage medium, the method comprises the following steps: unpacking and analyzing firmware of a to-be-detected embedded system to identify a script file and a template file; identifying a dependency relationship between script files and template files and interaction and dependency relationships between different types of script files, marking sensitive input source points and potential vulnerability sink points, and generating a control flow graph and an abstract syntax tree by adopting a context-sensitive control flow analysis algorithm; constructing a power set lattice according to an assignment statement in the control flow graph, and performing data flow analysis by using a fixed point algorithm to obtain a data flow constraint relationship; and performing taint analysis to identify all dangerous execution paths. The script security vulnerability can be automatically identified, the security of the embedded system in the script execution process is improved, and potential security threats are effectively prevented.

A computer-implemented method for generating input polynomial coefficients for use by a signal generator in a quantum control system. The signal generator is configured to generate an output signal which approximates a desired signal using fixed-point arithmetic and the input polynomial coefficients. The desired signal is segmented into a plurality of segments, the desired signal having one or more signal values in each segment. The method comprises, for each segment of the plurality of segments, obtaining one or more initial trial polynomial coefficients, and providing the one or more initial trial polynomial coefficients to an optimisation algorithm to generate the input polynomial coefficients. The optimisation algorithm comprises minimising a difference between the one or more signal values of the desired signal and one or more approximated signal values generated using an approximation function. The approximation function approximates the one or more signal values of the desired signal using the one or more initial trial polynomial coefficients and the fixed-point arithmetic used by the signal generator.

The present disclosure provides neural network computing methods and systems that can reduce hardware cost. A neural network computing method of one embodiment can include receiving a neural network model comprising asymmetric operations, each asymmetric operation comprising one or more fixed-point operands that are derived from corresponding floating-point operands through asymmetric quantization; compiling, by a compiler, a given asymmetric operation of the neural network model into a symmetric operation comprising a combined offset value, wherein the combined offset value is a constant computed by the compiler by at least merging zero points of inputs and outputs of the given asymmetric operation; and generating a symmetric neural network model comprising the symmetric operation for execution in a fixed-point algorithm by inference hardware.

Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.

The present invention discloses a Spectre vulnerability protection system and method based on a fixed point algorithm, relates to the technical field of computer attack security analysis, and solves the technical problem that the prior art fails to distinguish whether a protection statement has a Spectre vulnerability when protecting a program, thereby causing excessive statement protection and program performance loss. The present invention obtains a program from a database, marks the program with sensitive information, and tracks the sensitive information based on a bit granularity algorithm to obtain a secret value; retrieves a security domain, sets a security level label, determines a state domain according to the security level label, sequentially executes the program to obtain an over-estimated value; calculates a state transition of the program configuration according to the over-estimated value; obtains the protected program based on a fixed point, and adds defense to the program according to the fixed point; the present invention specifically protects statements that may cause Spectre vulnerabilities, thereby reducing performance loss caused by protection.

Disclosed herein is a computer-implemented method for generating input polynomial coefficients for use by a signal generator in a quantum control system. The signal generator is configured to generate an output signal which approximates a desired signal using fixed-point arithmetic and the input polynomial coefficients. The desired signal is segmented into a plurality of segments, the desired signal having one or more signal values in each segment. The method comprises, for each segment of the plurality of segments, obtaining one or more initial trial polynomial coefficients, and providing the one or more initial trial polynomial coefficients to an optimisation algorithm to generate the input polynomial coefficients. The optimisation algorithm comprises minimising a difference between the one or more signal values of the desired signal and one or more approximated signal values generated using an approximation function. The approximation function approximates the one or more signal values of the desired signal using the one or more initial trial polynomial coefficients and the fixed-point arithmetic used by the signal generator.