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Equation 1 · Part 4 · Edge AI Electronics and Sensor Systems in Practice: Quantization, Power Budgets, and Sensor Front Ends

Offset from zero

r=S(q−Z),r = S \left( q - Z \right),
q−Zq-Z

What this part means

Count how many integer steps q lies above or below the zero-point. A positive result gives a positive model value; a negative result gives a negative one.

Its job in the formula

This part belongs to the expression shown above. Read it together with the other parts of the formula.

The passage around this formula

A model trained in 32-bit floating point does not run on a microcontroller with a few hundred kilobytes of RAM and no floating-point unit worth using for anything but the occasional scalar. The standard fix is quantization: representing weights and activations as low-bit integers, most commonly 8-bit, and executing the forward pass using integer arithmetic end to end. The scheme that made this practical for commodity hardware is an affine mapping between a real value and its stored integer, r=S(q−Z)r = S \left( q - Z \right). where r is the real-valued number, q is the stored integer, S > 0 is a scale factor, and Z is an integer zero-point chosen so that real zero maps exactly onto a representable integer…

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Learn the underlying idea

The zero-point is the integer code chosen to mean the real value zero.

Open the illustrated zero-point z guide →

Sources cited in the surrounding passage

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

Further reading for this equation