Equation 5 · Edge AI Electronics and Sensor Systems in Practice: An Advanced Technical Guide
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Here is the fraction of time the device spends in power state i (sleep, always-on monitoring, escalated inference, radio) and is the measured — not datasheet-typical — power draw in that state. This formulation is worth writing out because it exposes the one lever that dominates every other design decision: since is normally close to one and is normally the smallest term, the average power is overwhelmingly sensitive to the escalation rate — how often the cheap monitoring stage decides something interesting is happening — far more than to how efficient the expensive inference stage is once escalated. A system that escalates twice as often as…
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Here is the fraction of time the device spends in power state i (sleep, always-on monitoring, escalated inference, radio) and is the measured — not datasheet-typical — power draw in that state. This formulation is worth writing out because it exposes the one lever that dominates every other design decision: since is normally close to one and is normally the smallest term, the average power is overwhelmingly sensitive to the escalation rate — how often the cheap monitoring stage decides something interesting is happening — far more than to how efficient the expensive inference stage is once escalated. A system that escalates twice as often as necessary can double its average power even with a perfectly efficient inference core.
Sources cited in the article section
- [8] Hello Edge: Keyword Spotting on Microcontrollers ↗
- [10] Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks ↗
- [3] MLPerf Tiny Benchmark ↗
These citations give research context. Read each source to check which claims it supports.
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