← All parts of this equation

Equation 22 · Part 7 · How a Model Actually Gets Small Enough to Run on a Phone

Symbol hatx

xq=clip(⌊xs⌉+z, qmin⁡, qmax⁡),x^=s(xq−z)x_q = \mathrm{clip}\left(\left\lfloor \frac{x}{s} \right\rceil + z,\ q_{\min},\ q_{\max}\right), \qquad \hat{x} = s\left(x_q - z\right)
x^\hat{x}

What this part means

the dequantised approximation actually used in arithmetic.

Its job in the formula

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

Where the article explains it

where ⌊\lfloor ⋅\cdot ⌉\rceil is round-to-nearest and x^\hat{x} is the dequantised approximation actually used in arithmetic.

The passage around this formula

The standard affine mapping takes a real value x , a scale s and a zero-point z , and produces an integer xq=clip(⌊xs⌉+z, qmin⁡, qmax⁡),x^=s(xq−z)x_q = \mathrm{clip}\left(\left\lfloor \frac{x}{s} \right\rceil + z,\ q_{\min},\ q_{\max}\right), \qquad \hat{x} = s\left(x_q - z\right). where ⌊\lfloor ⋅\cdot ⌉\rceil is round-to-nearest and x^\hat{x} is the dequantised approximation actually used in arithmetic. Jacob and colleagues’ integer-arithmetic scheme, still the reference formulation for this mapping, showed that with weights and activations quantized this way, “inference can be carried out using integer-only arithmetic” end to end on ordinary integer hardware, with no floating-point unit required at all, which is what makes the technique valuable on the cheapest edge silicon rather than merely on the largest [ 7 ] .

Read this part in the article →

Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

Open the illustrated variables: a letter stands for a value guide →

See this notation across published equations →

Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.