Estimates, Spreadsheets And Abstract Models

Our understanding of new designs hinges on the accuracy of models.


By Jon McDonald
Lies, damn lies and statistics. Occasionally I get the impression that some engineers feel we’ve just taken that step beyond statistics in our ESL modeling.

In a recent discussion I was very pointedly reminded of the subjective nature of abstract design and analysis. Like most predictions, you don’t really know what’s going to happen until it actually comes to pass. Using abstract modeling, ESL design is trying to predict what will happen when you build something before building it. No one has a perfect understanding of things that have not yet been done, but we can make educated decisions based on current knowledge and refine our decisions as more information becomes available.

This is a very important concept to keep in mind as we go through our design processes. We can start with estimates, guesses by another name, of our expected system capabilities. We can estimate performance and power characteristics to make some very early decisions on a project’s viability and the best approach for the design tradeoffs. As we get more detail on what we will be doing we may build spreadsheets to capture some of the dependencies between our choices. As our system understanding increases we may come to a point at which the number of variables and the interaction of these variables is too complex to effectively maintain in a spreadsheet.

At this point creating an abstract model of our system may make sense. With an abstract model we can start to explore much more detailed analysis, we can create models that allow us to understand the detailed system performance and power consumption of our system. Our models may be able to process realistic data sets, run software and exploring the efficiency of our hardware platform for its intended task.

All of this gives us a fantastic amount of information and understanding of our target design, but it is critical to remember that the accuracy of our understanding is limited by the accuracy of our models. We need to remember this fact and understand that knowledge of our design is continuously being refined. As we learn more from our decisions moving through implementation, we need to verify the effects of these implementation decisions on our earlier assumptions and models. Creation of an abstract platform for a new design is not a single exercise completed and frozen as we move to the next step. It is a continuous process of improving the accuracy of our abstract platform to reflect the decisions and trade-offs identified as we go through implementation.

Mark Twain popularized the disparaging view of statistics. I believe this largely hinged on the perceived practice of claiming faulty conclusions by selectively misusing statistical data. If we hope to achieve the full value from our abstract modeling and avoid negative interpretations, we need to accept the fact that our abstract platforms are continuously evolving representations of our current understanding of our target implementation. The accuracy of the models at any given time is very subjective, subject to the current visibility we have into the implementation decisions we have yet to make.

–Jon McDonald is a technical marketing engineer for the design and creation business unit at Mentor Graphics.

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