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The [[Neural Networks|neural network]] consists of four layers. The input layer is heavily overparametrized, feeding in the [[Board Representation|board representation]] for various king configurations.
The efficiency of [[NNUE]] is due to [[Incremental Updates|incremental update]] the outputs of the input layer in [[Make Move|make]] and [[Unmake Move|unmake move]],
where only a tiny fraction of its neurons need to be considered <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531&start=1 Re: NNUE accessible explanation] by [[Jonathan Rosenthal]], [[CCC]], July 23, 2020</ref>. The remaining three layers with 256x2x32-32x32-32x1 neurons are computational less expensive, best calculated using appropriate [[SIMD and SWAR Techniques|SIMD instructions]] performing fast [[Word|16-bit integer]] arithmetic, like [[AVX2]] on [[x86-64]], or if available, [[AVX-512]].
=Strong Points=
