Artificial Intelligence Could Optimize Your Next Design: Page 3 of 3

Modern electronics design is increasingly revealing the inadequacies of simulation-based verification. But researchers believe machine learning holds the answer.

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  

Comments

Recalling the claims of Bob Pease, "simulation can never be more accurate than the model used", and so the question becomes one of how accurate will the model be. Machine learning and artificial intelligence may possibly come close a lot of the time, but is that good enough? When the purpose is to create a "box" that provides the same responses as the actual design, is it likely that the machine learning will cover enough? Many bugs seem to be sequence sensitive and even sensitive to the random

(continued from previous) timing between multiple tasks being handled in a context-switching environment. It is not likely that AI and machine learning will discover that problem, or even try to look for it. The "twitter-like" character limit is a royal pain!!!!!

Some purely analog electronic systems working at low frequency will be easy to describe rather accurately in this way, but in most electronic systems there is no direct relations between input and output. This can be systems with internal memory , or having internal dynamics, hence most of the systems. To automate this process without drowning in data we need AI tools having the relevant laws of physics built in (Newton, Maxwell ++). - a challenge for the AI-community

M.H. is certainly correct! But in addition, also those systems that have any sort of memory and have multiple inputs will be quite a challenge to emulate. So while the system described is interesting it may not be very useful. In-House fabrication may be a more secure option, even if it is more expensive. Maintaining adequate security is never trivial and seldom cheap.

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