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with a scale S and a zero-point Z chosen so that ordinary 8-bit integers can represent the values a trained network actually produces. The 2018 paper that formalized this scheme for mobile and embedded inference showed that training the network with this quantization in the loop, rather than quantizing a finished floating-point model after the fact, preserves accuracy far better, and that integer-only arithmetic — no floating-point unit required anywhere in the inference path — can be implemented efficiently on hardware that would otherwise need an expensive float pipeline just to run the same network [ 5 ] . Removing the float pipeline matters disproportionately at the edge, because area and power there are not amortized the way they are across a datacenter accelerator serving millions of requests; every square millimetre and every milliwatt is paid for by a single device with a single small battery.