AI-Generated Models: Finding the Right Balance Between Speed, Accuracy and Engineering Insight

Artificial intelligence is creating new opportunities to rethink one of the longstanding challenges in electronic design automation: how engineers create and use models.

Models are fundamental to electronic design. They allow engineers to represent complex physical behavior at a level of abstraction that makes simulation and analysis practical. But creating models that are accurate, computationally efficient and appropriate for a particular design task can require significant engineering effort.

AI has the potential to change that equation.

Rather than replacing traditional simulation or engineering expertise, AI-generated and AI-assisted models can provide another tool for navigating the tradeoff between simulation accuracy, execution speed and model-development cost. The real opportunity is not simply to create models faster. It is to create the right model for the engineering problem at hand.

Moving Beyond a One-Model Approach

Historically, abstract models have provided significant performance advantages compared with detailed physical simulation. But engineers often have limited flexibility in deciding exactly where they want to sit on the spectrum between speed and accuracy. As model-generation techniques become more industrialized, that could change.

With a well-established pipeline for creating models, engineers can begin selecting different levels of accuracy and computational cost depending on the task. During early exploration, for example, a fast model may provide enough information to evaluate a large design space. As the design matures, increasingly accurate simulation can be introduced. This approach is particularly valuable for optimization, where engineers may need to evaluate hundreds or thousands of potential design configurations.

Accelerating Analog Simulation

Analog design offers a compelling example of where AI-assisted modeling could have an impact. Detailed transistor-level, electromagnetic and other physics-based simulations can be computationally expensive. Yet engineers often need to run these simulations repeatedly while optimizing a design.

AI technologies such as surrogate modeling and model order reduction offer a different approach. Instead of running the highest-fidelity simulation for every iteration, engineers can create a faster representation of the underlying system and use it during portions of the design and optimization process. This can produce dramatic improvements in simulation speed.

It’s possible to achieve gains in the 100x to 1,000x range from applying model order reduction and similar surrogate techniques to time-intensive analog simulations. The same speed-versus-accuracy tradeoff extends beyond a single circuit block. RF, electromagnetic and thermal simulation all face the same pressure, just with more coupled variables to account for as a design's physical domains multiply. The tradeoff, of course, is accuracy. Accelerating a simulation means accepting some level of approximation. That makes understanding the purpose of the model essential.

That tradeoff isn't linear, either. The last few percentage points of accuracy are often disproportionately expensive to recover, and not every point on that curve is equally easy to reach. There's no simple slider for trading effort against outcome. AI-based methods add another option to that curve in that they let engineers scale effort against outcome more gradually than traditional simulation approaches allow.

During design optimization, a surrogate model might provide the speed required to explore a large number of alternatives. Once engineers identify promising candidates, they can return to higher-fidelity simulation for final verification. In other words, faster models and detailed simulation are complementary rather than competing technologies.

Engineering Knowledge Still Matters

AI also does not eliminate the need for domain expertise. Consider modeling something as familiar as an electronic filter. A generic neural network might learn to reproduce the filter’s apparent response with apparent impressive numerical accuracy. But a model that looks correct is not necessarily physically valid. It could, for example, violate important properties such as passivity.

Before trusting a modeling technique, an engineer should first check it against the physical properties the system is known to obey, not just how closely its output matches a target dataset. That distinction (a model that fits the numbers versus a model that's intrinsically physically valid) is also why simulated performance and measured performance need to be checked against each other. Some modeling techniques are passive and causal by construction, meaning those properties are guaranteed by the model's structure. Most AI-inspired approaches take a different path: They lean on large volumes of data to make a model's output fit those same properties, without any structural guarantee that it holds outside the data it was trained on.

A surrogate model trained purely on other simulations can drift from reality in ways that only show up once it's compared against real hardware behavior.

Incorporating knowledge of electronics and system behavior into the modeling methodology can prevent these problems. Physics and engineering knowledge therefore become important criteria when choosing both the modeling technique and the way its accuracy is evaluated. This highlights an important distinction between simply fitting data and building an engineering model.

The goal is not merely to reproduce a set of simulation results. The model must behave appropriately within its intended operating space.

Understanding the Upfront Cost

AI-generated models also are not free. Creating a surrogate or AI model typically requires running detailed simulations first to generate training data. In effect, some of the computational work is moved to the beginning of the process. That investment can make sense when the resulting model will be used repeatedly.

For example, an engineer may already have a baseline design and want to optimize it. Rather than running a computationally intensive simulation for every optimization step, the engineer could perform an initial set of simulations upfront, build a model, and then use that faster model throughout the optimization loop. The total number of high-fidelity simulations run doesn't necessarily go down — in some cases it goes up. What changes is when that computation happens: it's paid upfront, in a batch, rather than blocking the engineer's time at every step of the optimization. The economics depend on how the model will be used.

There is also a tradeoff between engineering time and simulation time. A largely generic machine-learning technique may require a very large training dataset. A modeling engineer who applies knowledge of the underlying physics may be able to construct an effective model with far fewer simulations.

There is a direct tradeoff between engineering time and simulation time. A largely automated, data-hungry approach might need on the order of 100,000 simulations to train a usable model, while a more traditional, physics-informed approach might get there with a fraction of that, say, 5,000 simulations, at the cost of a week or more of a modeling engineer's time up front. Neither path is free, and the right balance depends on how often the resulting model will actually be used.

The question becomes: Where should the engineering effort be spent?

Teams may choose to invest more modeling expertise upfront to reduce simulation requirements or they may choose a more automated approach and devote additional compute resources to generating training data. The right answer depends on the design, available resources and how frequently the resulting model will be used.

A Hybrid Future for Engineering Simulation

AI has the potential to make model creation more accessible and economical, opening the door to specialized models that previously would have been too expensive to develop. But the greatest value is likely to come from combining AI with established engineering knowledge and high-fidelity simulation.

Fast AI-assisted models can help engineers explore larger design spaces and accelerate optimization. Physics-based knowledge can ensure those models behave appropriately. And detailed simulation can provide the final level of confidence required for verification.

Rather than asking whether AI will replace traditional simulation, a more useful question may be: How can AI help engineers use the right level of simulation at the right point in the design process?

Finding that balance between speed, accuracy, compute resources and engineering expertise will be key to realizing the full potential of AI-assisted modeling in EDA.

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