The present invention discloses a method for viewing simulation signals of digital products and a simulation system, the method includes: when performing FPGA simulation on digital products, reading out all external port status data of digital products in real time and recording, meanwhile reading out all internal status data of digital products once every interval time and recording; after completing the simulation, when needing a back trace to check the data of digital products in a certain clock cycle, reading out the internal status data of digital products stored at the last time point before this clock cycle and the external port status data at said time point in the recorded simulation data; and taking the read-out data as an initial status data of the FPGA, then reading out all internal status data of digital products clock by clock until running to the clock cycle that needs to be viewed.

A system and method for providing formal property verification across circuit design versions is described. In one embodiment, the system receives a first version and a second version of a circuit design. The received first version has a first set of constraints, a first set of next-state functions representing the first version of the circuit design, and a first property that has been verified as true for the first version of the circuit design. The received second version has a second set of constraints, a second set of next-state functions representing the second version of the circuit design, and a second property for the second version of the circuit design. The described embodiments further construct a composite circuit design based on the first set of constraints, the first set of next-state functions, and the first property and further based on the second set of constraints, the second set of next-state functions, and the second property. A third property is constructed for the composite circuit design in which the first property implies the second property. Some described embodiments output a proof or a counterexample for the second circuit design, based on the proof of the third property for the composite circuit design, since a user of the system and method is trying to verify the second circuit design, not the composite circuit design.

A system and method for providing formal property verification across circuit design versions is described. In one embodiment, the system receives a first version and a second version of a circuit design. The received first version has a first set of constraints, a first set of next-state functions representing the first version of the circuit design, and a first property that has been verified as true for the first version of the circuit design. The received second version has a second set of constraints, a second set of next-state functions representing the second version of the circuit design, and a second property for the second version of the circuit design. The described embodiments further construct a composite circuit design based on the first set of constraints, the first set of next-state functions, and the first property and further based on the second set of constraints, the second set of next-state functions, and the second property. A third property is constructed for the composite circuit design in which the first property implies the second property. Some described embodiments output a proof or a counterexample for the second circuit design, based on the proof of the third property for the composite circuit design, since a user of the system and method is trying to verify the second circuit design, not the composite circuit design.

This disclosure relates to signal observability rating. In an example, a method can include propagating a clock signal through a respective module of a circuit design in a forward and backward direction, evaluating clock signal propagation results for the respective module based on a forward and backward clock signal propagation of the clock signal to compute an observability rating for a data signal to be processed by the respective module during formal verification, and updating a current observability rating of the respective property for the data signal to the computed observability rating.

This disclosure relates to signal observability rating. In an example, a method can include propagating a clock signal through a respective module of a circuit design in a forward and backward direction, evaluating clock signal propagation results for the respective module based on a forward and backward clock signal propagation of the clock signal to compute an observability rating for a data signal to be processed by the respective module during formal verification, and updating a current observability rating of the respective property for the data signal to the computed observability rating.

A method of implementing an automated technology for conducting functional safety (FuSa) diagnostic coverage is provided. The method can include receiving functional safety information that includes failure modes defining wrong values of a signal indicating a factor manifesting an error, receiving an identification of internal safety protected signals and a diagnostic coverage for the FuSa block, performing back tracing of possible paths for an output port of a FuSa block for each failure mode of each safety protected signal, determining an area for each possible path, and determining, based on a diagnostic coverage and area calculated for each of the paths, a diagnostic coverage for each failure mode of the FuSa block.

A machine learning model predicts the hardness of unsolved properties. For example, the machine learning model may predict the relative hardness of pairs of properties—i.e., which property in the pair is harder to solve. These hardness predictions may then be used to formulate a priority order for a formal verification process to attempt to solve the unsolved properties. As the formal verification process progresses, it generates results. For example, certain properties may be solved. These results are used to update a training set, which is used to further train the machine learning model. The machine learning model is trained at runtime with incremental fine-tuning as the formal verification process progresses.

A machine learning model predicts the hardness of unsolved properties. For example, the machine learning model may predict the relative hardness of pairs of properties—i.e., which property in the pair is harder to solve. These hardness predictions may then be used to formulate a priority order for a formal verification process to attempt to solve the unsolved properties. As the formal verification process progresses, it generates results. For example, certain properties may be solved. These results are used to update a training set, which is used to further train the machine learning model. The machine learning model is trained at runtime with incremental fine-tuning as the formal verification process progresses.

A hardware monitor arranged to assess performance of a hardware design for an integrated circuit to complete a task. The hardware monitor includes monitoring and counting logic configured to count a number of cycles between start and completion of the symbolic task in the hardware design; and property evaluation logic configured to evaluate one or more formal properties related to the counted number of cycles to assess the performance of the hardware design in completing the symbolic task. The hardware monitor may be used by a formal verification tool to exhaustively verify that the hardware design meets a desired performance goal and/or to exhaustively identify a performance metric (e.g. best case and/or worst case performance) with respect to completion of the task.

A hardware monitor arranged to assess performance of a hardware design for an integrated circuit to complete a task. The hardware monitor includes monitoring and counting logic configured to count a number of cycles between start and completion of the symbolic task in the hardware design; and property evaluation logic configured to evaluate one or more formal properties related to the counted number of cycles to assess the performance of the hardware design in completing the symbolic task. The hardware monitor may be used by a formal verification tool to exhaustively verify that the hardware design meets a desired performance goal and/or to exhaustively identify a performance metric (e.g. best case and/or worst case performance) with respect to completion of the task.