Equation 12 · Comparing the Main Approaches to Edge AI Electronics and Sensor Systems
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Symbol P_standby
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The third is whether always-on microcontroller-class inference is fundamentally a wake-up filter for a more capable downstream accelerator, or an increasingly capable endpoint in its own right. The wake-up-filter view is supported by the duty-cycle model above: for the rare-trigger workloads this category is classically used for, dominates average power regardless of how much the active-phase model can do, so there is limited incentive to make it much more capable. The endpoint view is supported directly by MCUNet’s results, which push a real classification task past 70% ImageNet top-1 accuracy on commodity microcontroller memory budgets until recently assumed adequate…
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The third is whether always-on microcontroller-class inference is fundamentally a wake-up filter for a more capable downstream accelerator, or an increasingly capable endpoint in its own right. The wake-up-filter view is supported by the duty-cycle model above: for the rare-trigger workloads this category is classically used for, dominates average power regardless of how much the active-phase model can do, so there is limited incentive to make it much more capable. The endpoint view is supported directly by MCUNet’s results, which push a real classification task past 70% ImageNet top-1 accuracy on commodity microcontroller memory budgets until recently assumed adequate only for keyword spotting or simple gesture detection [ 6 ] . Both trends are advancing at once — chips are getting more efficient at near-zero idle power, and software is making the active phase more capable at fixed memory — and which effect dominates a given product’s design choice depends on whether its bottleneck is standby energy or on-device capability.
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