Abstract: A method of verifying connectivity in a circuit design, includes, in part, receiving a netlist of the circuit design; designating a plurality of destination nodes associated with the netlist; for each of the plurality of destination nodes, identifying one or more source nodes that are traversed from the destination node; for each source node identified as traversed from the destination node: transforming the netlist by including a first multiplexer having a first input terminal receiving a first variable logic value and an output terminal coupled to the source node; and enabling the first multiplexer to pass the first variable value to the destination node from the source node in order to check for connectivity between the source node and the destination node.

Abstract: A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.

Abstract: A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.