Integer Quantization for Deep Learning Inference: Principles and EmpiricalEvaluation

How 8-bit Quantization Shrinks AI Models and Speeds Up Inference

Imagine your phone or laptop running smart apps faster while using less battery, that is what this method does.
By turning parts of a neural net into 8-bit numbers, models become much smaller and they run with more speed.
The trick works for many tasks like photos, speech and text, and it keeps results nearly the same as before.
In tests it was able maintain accuracy within about one percent of the usual full-precision versions, so you don’t lose much.
This means big models that are hard to shrink, like MobileNets and BERT-large, can still work well on everyday devices.
The change also lets chips use fast integer math so inference gets a real boost, and servers handles more requests at once.
Engineers can follow a simple workflow to convert models, and the end result is models that take less space, run faster and save energy.
It may sound small but the impact on apps and devices is big, and many companies are already trying it out.

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Integer Quantization for Deep Learning Inference: Principles and EmpiricalEvaluation

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