Approximating tanh in ML: Padé approximation, K-TanH, and IEEE-754 bit-hacks

Neural networks execute millions of tanh operations per forward pass, making this function a major optimization target.

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Three approaches compete: Padé approximation (mathematical precision), K-TanH (speed-precision balance), and IEEE-754 bit-hacks (brute force).

Choice depends on target hardware and energy budget: GPU affine, TPU, or CPU with limited memory margin.

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