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Equation 10 · Comparing the Main Approaches to Edge AI Electronics and Sensor Systems

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Nactive(t)N_{\mathrm{active}}(t)

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NactiveN_{\mathrm{active}}

Symbol N_active

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tt

Symbol t

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subscript

subscript

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where Nactive(t)N_{\mathrm{active}}(t) , the number of pixels crossing threshold at a given instant, is normally far smaller than the total pixel count NpixelsN_{\mathrm{pixels}} for a mostly-static scene. That scene-dependence is also the sensor’s clearest limitation rather than a free efficiency gain: because each pixel’s operating principle is defined by Gallego and colleagues as asynchronously measuring per-pixel brightness change and encoding “the time, location and sign” of that change [ 3 ] , a highly textured, fast-moving, or vibrating scene drives Nactive(t)N_{\mathrm{active}}(t) toward NpixelsN_{\mathrm{pixels}} , and the bandwidth and power advantage over a frame sensor narrows or disappears for exactly the scenes…
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where Nactive(t)N_{\mathrm{active}}(t) , the number of pixels crossing threshold at a given instant, is normally far smaller than the total pixel count NpixelsN_{\mathrm{pixels}} for a mostly-static scene. That scene-dependence is also the sensor’s clearest limitation rather than a free efficiency gain: because each pixel’s operating principle is defined by Gallego and colleagues as asynchronously measuring per-pixel brightness change and encoding “the time, location and sign” of that change [ 3 ] , a highly textured, fast-moving, or vibrating scene drives Nactive(t)N_{\mathrm{active}}(t) toward NpixelsN_{\mathrm{pixels}} , and the bandwidth and power advantage over a frame sensor narrows or disappears for exactly the scenes where an application often needs the most from its sensor. The advantage is real and large for a mostly-static scene with occasional motion; it is not a property of the device independent of what it is pointed at.

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