Complex tasks can now be completed faster and more accurately, and failures can now be predicted and corrected more reliably before they occur. The artificial intelligence technologies used to achieve all this, and how to apply them to even more tasks, are the subject of Siemens Digital Industries Software's latest white paper. This comprehensive white paper, a must-read for anyone studying electronic systems design, provides a complete picture of our ever-changing world. Let's first examine Siemens Digital Industries' perspective on artificial intelligence and its impact on system design.
Artificial intelligence tools in the industry
The term "artificial intelligence" has a very broad meaning. It represents a collection of algorithms and information processing strategies. Many of these concepts have existed for a long time, some dating back to the 1940s. The combination of new application strategies, supported by enormous processing power, has created the revolution we are witnessing today. According to the white paper:
Over the past decade, artificial intelligence (AI) has evolved from a visionary concept into a mainstream reality for many large companies.
The white paper outlines the technologies at play in this revolution and how they combine to solve real-world problems. It is recommended that you obtain firsthand information about all of this from the white paper. These technologies are interwoven through mathematics, computer science, statistics, and psychology. They include machine learning and deep learning. The goals of artificial intelligence applications are broad, including:
• Make informed decisions and improve efficiency;
• Complete daily tasks with minimal effort and improve work efficiency;
• Enhance expertise by recommending the next task;
• Application of AI in PCB design.
The white paper uses PCB design as an example to illustrate the impact of artificial intelligence. PCB design presents engineers with the challenge of providing sufficient power and cooling for complex, fast ICs while maintaining the signal and thermal integrity of every high-speed signal between the various ICs on the board. The complexity of the problem can explode rapidly. This application provides a good context to see the various ways artificial intelligence can transform design.
The white paper discusses the many impacts of artificial intelligence on the design process. Here is a brief summary:
The learning curve: Experienced engineers can intuitively choose the best application tools and settings options. This is the main reason why senior engineers are far more productive than junior engineers. What if artificial intelligence could capture this intuition, allowing junior engineers to work like senior engineers?
Component selection: Engineers spend a significant amount of time researching component selection to best meet system requirements. What if a model based on historical information could greatly reduce selection time?
Component model creation: Generating models to represent components (e.g., symbols, 2D/3D physical geometry, and simulation models) is time-consuming and requires many different skills. What if natural language processing, image recognition, and machine learning could be applied here?
Schematic connections: Optimal component placement and connections require a broad design perspective. This is another opportunity for artificial intelligence to play a role.
Dynamic reuse: Once a design is complete, the knowledge applied to that design is often lost. What if this knowledge could be saved and managed?
Constraints: Similarly, prior knowledge can make this task easier and yield higher quality results.
Layout: These tasks use heuristics to optimize automated processes. What if artificial intelligence could make these methods more specific and accurate?
Analysis and validation: The design sensitivity to factors such as material properties, physical layout, and temperature/voltage makes this process challenging. What if artificial intelligence could distill these interdependencies into a more predictable model?
Design Synthesis: Combining all of these together will have a huge impact on generating AI approaches.
Big picture perspective
Siemens Digital Industries software has a very broad reach, serving customers across numerous markets, industries, and application areas. This white paper discusses some of the investments made in deploying artificial intelligence across this broad spectrum.
Examples discussed include:
• AI/ML-based edge application accelerators reach the market faster;
• Optimization of efficient decoupling capacitor banks in power transmission networks based on genetic algorithms;
• An adaptive UI that improves user productivity by predicting the commands users are most likely to use;
• It provides next-step suggestions with an accuracy rate of up to 90% in microflow.
This is a widely applicable suite of AI applications covering design, manufacturing, and production. As part of the Siemens Xcelerator portfolio, these tools help electronic systems design companies leverage artificial intelligence to bring future products to market.
