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Equation 13 · What an AI Accelerator Actually Is: Silicon, Packaging, and the Memory It Can Reach

What does this equation mean?

Pattainable=min⁡(Pmax⁡,  β⋅I).P_{\mathrm{attainable}} = \min\left(P_{\max},\; \beta \cdot I\right).

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Inputs and operationsmin(P_max, β × I)
Result or conditionP_attainable
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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PattainableP_{\mathrm{attainable}}

Symbol P_attainable

attainable performance.

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Pmax⁡P_{\max}

Symbol P_max

the machine’s peak arithmetic rate.

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β\beta

Symbol β

the achievable memory bandwidth.

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II

Symbol I

I appears in the objective or constraint used by the optimization on the right.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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multiplication

multiplication

Multiply the quantities on either side.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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How to interpret it

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What the article says around this equation

with W operations and Q bytes. Let Pmax⁡P_{\max} be the machine’s peak arithmetic rate and β\beta its achievable memory bandwidth. Then attainable performance is bounded by Pattainable=min⁡(Pmax⁡,  β⋅I)P_{\mathrm{attainable}} = \min\left(P_{\max},\; \beta \cdot I\right). That expression is the whole model, and it has one structural feature that matters more than the rest. The two bounds cross at a ridge point

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