An efficiently updatable neural network (NNUE, a Japanese wordplay on Nue, sometimes stylised as ƎUИИ) is a neural network-based evaluation function whose inputs are piece-square tables, or variants thereof like the king-piece-square table.
[2] While being slower than handcrafted evaluation functions, NNUE does not suffer from the 'blindness beyond the current move' problem.
[6][7] Since 2021, many of the top rated classical chess engines such as Komodo Dragon have an NNUE implementation to remain competitive.
[10][11] The neural network used for the original 2018 computer shogi implementation consists of four weight layers: W1 (16-bit integers) and W2, W3 and W4 (8-bit).
It has 4 fully-connected layers, ReLU activation functions, and outputs a single number, being the score of the board.