Abstract: Certain aspects of the present disclosure provide techniques and apparatus for evaluating electronic circuit designs. A directed graph representing a netlist design for an electrical circuit is accessed, the netlist design comprising a plurality of electronic components and a plurality of connections among the plurality of electronic components. A node in the directed graph is selected, the node corresponding to a register that receives input from one or more of the plurality of electronic components in the netlist design. A subgraph is generated for the node, based on the directed graph, comprising identifying a connectivity cone ending at the first register. A functional embedding is generated for the subgraph based on a trained encoder machine learning model. A predicted performance characteristic of the netlist design is generated based at least in part on the functional embedding.

Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.

Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.