Abstract: Optimizing ML models for resource constraints in electronic design automation (EDA) computer aided design (CAD) flows, including computing a set of bin thresholds based on slope changes in an ordered set of discrete probabilistic classification scores, assigning the discrete probabilistic classification scores to the bins based on the values of the discrete probabilistic classification scores and the bin thresholds, and selecting processes associated with the discrete probabilistic classification scores of one or more of the bins based on costs of the respective processes and a global budget.

Abstract: Embodiments relate to a system for translating design specifications of an electronic circuit. In one embodiment, the design specification is parsed to identify one or more sentences. From the one or more identified sentences, the system extracts semantic concepts. Additionally, for each sentence of the one or more identified sentences, the system determines whether the sentence is translatable. If a target sentence is translatable, the system generates a parse tree for the target sentence, and generates a probabilistic shift-reduce schedule for the generated parse tree. Using the generated probabilistic shift-reduce schedule and optionally the generated parse tree, the system generates code for verifying the condition specified in the target sentence. In some embodiments, to generate the code, the system parses the target sentence using the generated probabilistic shift-reduce schedule.

Abstract: A design problem can include a mixture of hard constraints and soft constraints. The soft constraints can be prioritized and the design problem solved. One or more soft constraints may not be honored in the midst of the solving of the design problem. Debugging can be performed and the unsatisfied soft constraints identified. Root-cause analysis can evaluate the challenges within the design problem which caused soft constraints not to be honored.

Abstract: A design problem can include a mixture of hard constraints and soft constraints. The soft constraints can be prioritized and the design problem solved. One or more soft constraints may not be honored in the midst of the solving of the design problem. Debugging can be performed and the unsatisfied soft constraints identified. Root-cause analysis can evaluate the challenges within the design problem which caused soft constraints not to be honored.

Abstract: This invention relates to a method and apparatus for combined stuck-fault testing and partial scan delay-fault built-in self testing (BIST). For partial scan delay-fault BIST, the circuit is modeled for breaking all flip-flop feedback cycles in the circuit. A selection of flip-flops to be scanned to break all sequential cycles is determined from an optimal feedback vertex set. A digest, devour and tidy-up (DDT) heuristic can be used on a weighted signed graph formed from an S-graph of the circuit to determine an optimal feedback vertex set. Determined partial scan delay fault BIST hazards can be removed from the circuit by inserting parity flippers to invert selected paths during testing. The same DDT heuristic can be used to determine optimal placement of the parity flippers in the circuit.