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Published equation contexts

Pavg=d⋅Pactive+(1−d)⋅PsleepP_{\text{avg}} = d \cdot P_{\text{active}} + (1-d) \cdot P_{\text{sleep}}

Why this formula appears here

and the sustainable average power of an always-on system operating at duty cycle d (the fraction of time spent active rather than in a low-power sleep state) is Pavg=d⋅Pactive+(1−d)⋅PsleepP_{\text{avg}} = d \cdot P_{\text{active}} + (1-d) \cdot P_{\text{sleep}}. This identity is trivial algebraically, but it is exactly the trade a systems designer is making when choosing between “a bigger, slower model that runs less often” and “a smaller, faster model that still meets the same detection latency.” Neither PactiveP_{\text{active}} nor tactivet_{\text{active}} is fixed by the model architecture alone; both depend on the accelerator the model runs on.

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PavgP_{\text{avg}}

Symbol P_avg

PaP_avg is part of the quantity the equation computes from the expression on the right.

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PactiveP_{\text{active}}

Symbol P_active

PaP_active is one of the signed contributions combined to compute the quantity on the left.

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PsleepP_{\text{sleep}}

Symbol P_sleep

PsP_sleep is one of the signed contributions combined to compute the quantity on the left.

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Published contexts (1)

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Pavg=d⋅Pactive+(1−d)⋅PsleepP_{\text{avg}} = d \cdot P_{\text{active}} + (1-d) \cdot P_{\text{sleep}}

Equation 7 · Edge AI & Electronics

How Edge AI Electronics and Sensor Systems Actually Work

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

and the sustainable average power of an always-on system operating at duty cycle d (the fraction of time spent active rather than in a low-power sleep state) is Pavg=d⋅Pactive+(1−d)⋅PsleepP_{\text{avg}} = d \cdot P_{\text{active}} + (1-d) \cdot P_{\text{sleep}}. This identity is trivial algebraically, but it is exactly the trade a systems designer is making when choosing between “a bigger, slower model that runs less often” and “a smaller, faster model that still meets the same detection latency.” Neither PactiveP_{\text{active}} nor tactivet_{\text{active}} is fixed by the model architecture alone; both depend on the accelerator the model runs on.

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