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Properly time-distributed stimuli - Part III

A use case example

I was working on a project in which not only the input data was time-stamped, but moreover, the density of the timestamps greatly affected the computation delay in terms of clock cycles. The project was performance critical and careful models of performance were needed. A very careful generation of the time stamps according to an appropriate model was necessary for the simulations to give us the results we needed, and to finally verify our performance models.

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Properly time-distributed stimuli - Part II

Basics about statistical distributions for events random in time

Poisson Process

We will study the case of the input events being described by a Poisson Process, the most common case for events random in time.

Without going into mathematical formalism, roughly described, events adhering to the following conditions can be labeled to be generated by a Poission Process:

  • The time of an event is independent of the time of the previous event
  • The events have a fixed average rate

Some examples of phenomena well-modeled by a Poisson Process are:

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Properly time-distributed stimuli - Part I

Introduction and summary

Finding any bugs or problems in simulations rather than in hardware tests is generally a big time-saver. Some designs will depend on how external input are distributed in time (control signals, input data write/fetches or time-stamped data) and in those cases a good model for those events is sometimes desired.

For events "random in time", we will see that the so-called Poisson Process-related distributions such as the Exponential or the Poisson Distribution can be used. We will also see that time deltas or absolute times of such distributions can be generated rather easily and computing-efficient.

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