Publikation

Knowledge Graph based Electrical Circuit Simulation and Component Selection

Mizanur Rahman Syed, Johannes Bayer

DATA 7/2021.

Abstrakt

Electrical circuits can be considered graph structures with components (like resistors, capacitors or inductors) as nodes and wiring as edges. For simulation and hardware implementation purposes, these nodes are equipped with attributes like electrical characteristics and referenced against libraries of real-world products. The presented system takes an RDF representation of a netlist and uses Ngspice to calculate circuit parameters. Additional parameters can be specified using formulas which are also represented in RDF. The parameters and signals calculated for components are then used as constraints to shortlist candidates from the product knowledge graph and shortlisted candidates can then be optimized for cost if the marginal costs of procurement for the products are known. Signal and device characteristics matching criteria as well as unit standardization formulas are also stored as RDF triples to simplify the addition of new device types, circuit characteristics and matching criteria. A circuit simulator is used to predict the voltages at nodes and current flows in wires. These values and parameters are substituted in formulae to derive additional values such as the component's power consumption. Formulae are specified in RDF and the system checks which of the specified formulae for a component can be applied given the set of known parameters. These values and parameters are added as an enrichment to the RDF representation of the circuit, which is then used to shortlist products. Product information for components such as resistance, capacitance, power output and prices are collected from web stores to build a knowledge graph of different device types. Multiple physical devices from various manufacturers and vendors, with differing parameters and physical characteristics can match component requirements known at this stage. This is achieved by filters for each known parameter for a circuit component to list the most suitable device matches. Component level shortlists of matching devices from the knowledge graph also provide the engineer pricing information extracted from vendor sites. While detailed costing for the final hardware implementation and cost optimization is still not achievable because of complicated pricing rules and ordering costs, the designer is given an overview of the potential options along with the pricing per piece and the minimum order size. In future, the system is envisioned to support the engineer by automated constraint checking and product recommendations for implementing and altering circuits.

Projekte

Data_2021_Poster.pdf (pdf, 221 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence