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Predictable Verification; Part 3

Bryan Dickman & Joe Convey

21 Feb 2024

"Increase testbench efficiency to increase volume of testing and thereby improve quality"

In Predictable Verification; Part 3 we explore what happens if you choose to keep the cost and the timescale the same level as in the "understanding the baseline" scenario below (Figure 1).

As we saw previously, verification was happening too late in the project resulting in many bugs being found late and possibly ending up in final deliveries to customers. A clear justification for a shift-left strategy to be adopted as discussed in Predictable Verification Part 2.

New scenario; Use testbench efficiency gains to significantly increase the volume of testing to find more bugs, earlier.

Analytics and data modelling can be used to simulate keeping cost and effort at the same level as the baseline, whilst experimenting with a higher degree of verification.

What efficiency gains result in enough extra cycles being run that lead to higher quality IP (i.e. less post-release bug escapes)?


An example of this can be seen in the bug rate data (More Cycles Figure 2 below), where there is a significant peak of bug finding in the beta phase, but much less bugs being found in the final phase and evidence of a much more stable design.

Figure 2 - The "More Cycles" scenario - more cycles within the same cost/time constraints

The overall cost for this scenario is roughly the same as baseline in Figure 1, but the number of tests run and the total number of cycles achieved has increased by 2.3X, and overall, more bugs have been found, thus reducing the risk of post-release bug escapes.


To achieve this the engineering team have invested even more effort in verification efficiency improvements achieving an overall improvement of 2.5X over the baseline case.

It's likely your priority is time to revenue with high product quality. The baseline scenario analytics (Figure 1) reveal insufficient verification testing cycles being performed to achieve the required quality level, the More Cycles scenario (Figure 2) allows you to model just how much extra testbench efficiency improvement effort is required in order to run massively more cycles within the same budget for $ and time. The message is clear;

Do significantly more with the same resources and the same delivery constraints.

In Predictable Verification Part 4. we consider the final scenario in which increased product complexity, resulting in a much bigger verification space will require more investment if EDA tools, engineering platform and resources.

To discuss your particular verification campaign challenges, Silicon Insights offer independent thought leadership, backed up with years of experience and insights in how to model senarios, exploit your data, and drive predictable delivery in your organisation.


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