Abstract: Systems, methods, logic, and devices may support machine learning-based anomaly detections for embedded software applications. In a learning phase, an anomaly model training engine may construct an anomaly detection model, and the anomaly detection model configured to provide a determination of whether the embedded software application exhibits abnormal behavior based on activity measure and application parameter inputs. In a run-time phase, an anomaly detection engine may sample the embedded software application to obtain an activity measure and application parameters during the run-time execution and provide, as inputs to the anomaly detection model, the activity measure and the application parameters sampled during the run-time execution. The anomaly detection engine may further determine whether the embedded software application exhibits abnormal behavior based on an output from the anomaly detection model for the provided inputs.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: A system and method are described for converting a circuit description into transaction-based description at a higher level of abstraction. Thus, a designer can readily view a series of transactions that occurred in the simulation of a circuit. In one aspect, the simulated signals are analyzed and converted into messages of a protocol used by the design. A combination of the messages represents a transaction. Thus, the simulated signals are then converted into a series of protocol transactions. In another aspect, a message recognition module performs the analysis of the simulated signals and converts the simulated signals into messages (e.g., request for bus, bus acknowledge, etc.). A transaction recognition module analyzes the messages and converts the messages into transactions (e.g., Read, Write, etc.). Using both the system and method the circuit description is converted into a higher level of abstraction that allows more comprehensive system-level analysis.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: In various implementations of the invention, methods and apparatuses are provided that enable timing accurate, bit level hardware models for simulation at a rapid rate. With various implementations of the invention, a functional module is combined with a timing module. The combination may be employed to assist in performing performance modeling. With various implementations of the invention, a functional module, a timing module, and a module wrapper are provided, the module wrapper having at least a slave and master port. The slave port and the master port allowing for the exchange of data between modules, between the module and a host computing environment, and between the module and a performance modeling platform.

Abstract: A system and method is disclosed for converting an existing circuit description from a lower level description, such as RTL, to a higher-level description, such as TLM, while raising the abstraction level. By changing the abstraction level, the conversion is not simply a code conversion from one language to another, but a process of learning the circuit using neural networks and representing the circuit using a system of equations that approximate the circuit behavior, particularly with respect to timing aspects. A higher level of abstraction eliminates much of the particular implementation details, and allows easier and faster design exploration, analysis, and test, before implementation. In one aspect, a model description of the circuit, protocol information relating to the circuit, and simulation data associated with the lower level description of the circuit are used to generate an abstract model of the circuit that approximates the circuit behavior.

Abstract: A system and method are described for converting a circuit description into transaction-based description at a higher level of abstraction. Thus, a designer can readily view a series of transactions that occurred in the simulation of a circuit. In one aspect, the simulated signals are analyzed and converted into messages of a protocol used by the design. A combination of the messages represents a transaction. Thus, the simulated signals are then converted into a series of protocol transactions. In another aspect, a message recognition module performs the analysis of the simulated signals and converts the simulated signals into messages (e.g., request for bus, bus acknowledge, etc.). A transaction recognition module analyzes the messages and converts the messages into transactions (e.g., Read, Write, etc.). Using both the system and method the circuit description is converted into a higher level of abstraction that allows more comprehensive system-level analysis.

Abstract: A system and method is described for generating a power model of a circuit from a lower level description, such as Gate-level or RTL. In one aspect, simulation data is converted into a series of messages or transactions. Power is then determined on a per message or per transaction basis. In another aspect, an abstract power model is generated using a neural network. The neural network generates a system of weighted equations representing an accurate power model.