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Artificial Intelligence Could Optimize Your Next Design

Artificial Intelligence Could Optimize Your Next Design
Modern electronics design is increasingly revealing the inadequacies of simulation-based verification. But researchers believe machine learning holds the answer.

The more complex modern electronic systems have gotten – the less comprehensive simulation has become as a design tool. But there's a solution on the horizon in the form of behavioral modeling based on machine learning. One of the leading centers behind this research is the Center for Advanced Electronics through Machine Learning (CAEML) at the University of Illinois at Urbana-Champaign. Funded by the National Science Foundation and formed with the aim of applying machine-learning techniques to microelectronics and micro-systems modeling, CAEML is already conducting research into several areas including: Design Optimization of High-Speed Links; Nonlinear Modeling of Power Delivery Networks; and Modeling for Design Reuse.

Elyse Rosenbaum

“The limitations in simulation that people experience have always been there. But people are trying to do more ambitious things. And we need more accurate models than we've had in the past,” Elyse Rosenbaum, director of CAEML told Design News in an interview. “For example, we make everything smaller. The physical accuracy of the models hasn't changed, but we're entering regimes where there's increasing cross talks between components simply because we're packing them together more closely.”

Rosenbaum, who will be delivering a keynote on machine learning and electronics modeling at DesignCon 2017, said new product demands, such as the push for greener technologies – which calls for ever-improving energy minimization – are creating an environment for design engineers in which simulation-based verification alone is simply not practical. “When you're designing a product, such as, say, a cellphone, you have maybe about a hundred or so components on the circuit board. That's a lot of design values. To completely explore that design space and try every possible combination of components is unfeasible. You'd never get your product out of the door,” Rosenbaum said.

The solution then for Rosenbaum and the researchers at CAEML is highly abstracted behavioral models that let engineers rapidly do a design space exploration to find an optimal sign, not just one that's good enough.

“When we want to do design optimization we can't be concerned with every single variable inside the system,” Rosenbaum said. “All we really care about is what's happening in the aggregate – the signals at the outside of the device where the humans are interacting with it. So we want these abstracted models and that's what machine learning gives you – models that you then use for simulation.”

Accomplishing this is no small task, given that simulations require engineers to model everything in a system, and all of those effects can be represented. What Rosenbaum and her team are seeking is completely data-driven modeling, not based on any prior knowledge of what's inside the system. To do this they need to use machine learning algorithms to that can predict a particular output and represent the behaviors of particular components.

 DesignConMachine Learning Overcomes Hurdles. In her keynote, NSF CAEML Director Elyse Rosenbaum will explore how machine learning can strengthen behavior modeling and provide a solution to simulation verification impracticalities. At DesignCon 2017 , Jan. 31 to Feb. 2 in Santa Clara, CA. Register here for the event, hosted by  Design News’parent company, UBM.

Beyond the potential for more comprehensive simulation Rosenbaum said machine learning-based modeling also offers several other benefits that should be attractive to companies, such as the ability to share models without revealing vital intellectual property (IP).

“Because behavior modeling only describes, say input/output characteristics, they don't tell you what's inside the black box. They preserve or obscure IP. With a behavioral model a supplier can easily share that model with their customer without disclosing proprietary information,” Rosenbaum explained. “It allows for the free flow of critical information and it allows the customer then to be able to design their system using that model from the supplier.”

Most integrated circuit manufacturers, for example, use Input/Output Buffer Information Specification (IBIS) models to share information about input/output (I/O) signals with customers, while also protecting IP. The problem, Rosenbaum said, is that IBIS models tell you absolutely nothing about the circuit design details.

“Where machine learning can help is to make models such as IBIS better,” Rosenbaum said. “IBIS models don't represent interactions between the multiple I/O pins of an integrated circuit. There's a lot of unintended coupling that current models can't replicate. But with more powerful methods based on machine learning for obtaining models, next-gen models may be able to capture those important effects.”

The other great benefit would be reduced time to market. In the current state of circuit design there's almost a sense of planned failure that eats up a lot of development time. “Many chips don't pass qualification testing and need to undergo a re-spin,” Rosenbaum said. “With better models we can get designs right the first time.”

Rosenbaum comes from a background in system level ESD, a world she said is built on trial and error and would benefit greatly from behavioral modeling. “[Design engineers] make a product, say a laptop, it undergoes testing, probably fails, then they start sticking additional components on the circuit board until it passes...and it wastes a lot of time,” she said. “They build in time to fix things, but it's often by the seat of one's pants. If we had accurate models for how these systems would respond to ESD we could design them to pass qualification testing the first time.”

The willingness and interest in machine learning-based behavioral models is there, but the hurdles are in the details. How do you actually do this? Today, machine learning finds itself being largely applied to image recognition, natural language processing, and, perhaps most ignominiously, the sort of behavior prediction that lets Google guess what ads it wants to serve you.

“There's only been a little bit of work in regards to electronics modeling,” Rosenbaum said. “We have to figure out all the details. We're working with real measurement data. How much do you need? Do you need to process or filter it before delivering it to the algorithm? And which algorithms are suitable for representing electronic components and systems? We have to answer all of those questions.”

CAEML's aim is to demonstrate, over a five-year period, that machine learning can be applied to modeling for many different applications within the realm of electronics design. As part of that the center will be doing foundational research on the actual machine learning on the algorithms – identifying ones that are most suitable and how to use them.

“Although we're working on many applications – signal integrity analysis, IP reuse, power delivery network design, even IC layouts and physical design – all of which require models, there are common problems that we're facing, a lot of them do pertain to working with a limited set of real measurement data,” Rosenbaum said. “Historically, machine learning theorists really only focused on the algorithm. They assumed there's an unlimited quantity of data available, and that's not realistic, at least in our domain. In order to get data you have to fabricate samples and measure them, which that's takes time and money. The amount of data, though it seems huge to us, is very small compared to what they use in the field. “

Chris Wiltz is the Managing Editor of Design News  

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