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Equation 6 · Part 2 · Edge AI Electronics and Sensor Systems in 2035: Scenarios, Signals, and Falsifiable Predictions

Symbol t

η(t)\eta(t)
tt

What this part means

the time or time index used in this relationship.

Its job in the formula

t is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Where the article explains it

If PbudgetP_{\text{budget}} is the (roughly fixed, application-set) power envelope in watts and η(t)\eta(t) is the achieved energy efficiency of the best available accelerator at time t , measured in tera-operations per second per watt, then the usable on-device compute throughput is approximately Cedge(t)C_{\text{edge}}(t) ≈\approx PbudgetP_{\text{budget}} ⋅\cdot η(t)\eta(t).

The passage around this formula

This is not a scaling law in the sense of a fitted curve; it is closer to an accounting identity, and its value is in what it rules out. Because PbudgetP_{\text{budget}} is nearly constant for a given device class, essentially all of the growth in on-device model capability that anyone can expect by 2035 has to come from growth in η(t)\eta(t) — from architecture, from process, from numerical precision, and, in the discontinuity scenario below, from a different physical mechanism entirely. Reuther and colleagues’ survey of commercial AI accelerators, which compiles peak-performance and power figures across dozens of parts and computes efficiency relative to that peak, documents exactly this pattern:…

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Sources cited in the surrounding passage

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