Equation 8 · Edge AI Electronics and Sensor Systems in 2035: Scenarios, Signals, and Falsifiable Predictions
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Symbol eta
the achieved energy efficiency of the best available accelerator at time t , measured in tera-operations per second per watt.
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itself is not free to grow indefinitely inside conventional CMOS. Horowitz, Alon, and Patil’s widely cited analysis of chip power scaling made the mechanism explicit: as feature size shrank across recent nodes, supply voltage and threshold voltage stopped scaling down at the historical rate, because pushing them lower drives subthreshold leakage up sharply, and power — not transistor count — became the binding limiter on how fast or how densely a chip could usefully run [ 11 ] . That single fact is the quiet premise behind most edge-AI marketing that promises “10x more efficient” silicon every generation: within one switching technology, each further gain in gets structurally…
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itself is not free to grow indefinitely inside conventional CMOS. Horowitz, Alon, and Patil’s widely cited analysis of chip power scaling made the mechanism explicit: as feature size shrank across recent nodes, supply voltage and threshold voltage stopped scaling down at the historical rate, because pushing them lower drives subthreshold leakage up sharply, and power — not transistor count — became the binding limiter on how fast or how densely a chip could usefully run [ 11 ] . That single fact is the quiet premise behind most edge-AI marketing that promises “10x more efficient” silicon every generation: within one switching technology, each further gain in gets structurally harder to extract, because the voltage floor that sets the energy cost of a single switching event is approached asymptotically rather than crossed. This is analysis built on a documented mechanism, not a forecast by itself — the forecast comes later, once the discontinuity question has been laid out.
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