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Equation 2 · Part 5 · Ten Failure Modes That Define Small and On-Device AI Deployments

Starting index or lower bound: i

Srequest≤∑ifreeiS_{\mathrm{request}} \le \sum_i \mathrm{free}_i
ii

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This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.

Its job in the formula

i appears in the bound of this sum. The bound states where the repeated operation starts, ends, or which values it includes.

The passage around this formula

is the condition that actually governs whether an allocation succeeds, and it can fail even when SrequestS_{\mathrm{request}} ≤\le ∑i\sum_i freei\mathrm{free}_i holds comfortably — the requested size fits inside total free memory but not inside any one free block. This is precisely the problem the smartphone inference system PowerInfer-2 was built to manage: it introduces a segmented neuron cache with a least-recently-used eviction policy specifically to keep the working set of “hot” and “cold” neuron clusters from fragmenting a mobile device’s limited DRAM, allowing models “whose sizes exceed the device’s memory capacity” to run without the allocator failures that naive loading produces, while cutting…

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Σ adds a collection of terms. Π multiplies them. The lower and upper labels tell you which terms belong to the collection.

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