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.[1] NNUE is used primarily for the leaf nodes of the alpha–beta tree.[2] While being slower than handcrafted evaluation functions, NNUE does not suffer from the 'blindness beyond the current move' problem.[3]
NNUE was invented by Yu Nasu and introduced to computer shogi in 2018.[4][5] On 6 August 2020, NNUE was for the first time ported to a chess engine, Stockfish 12.[6][7] Since 2021, all of the top rated classical chess engines such as Komodo Dragon have an NNUE implementation to remain competitive.
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.
NNUE evaluation functions for computer shogi on github.com
January 01, 1970
efficiently, updatable, neural, network, efficiently, updatable, neural, network, nnue, japanese, wordplay, sometimes, stylised, ƎuИИ, neural, network, based, evaluation, function, whose, inputs, piece, square, tables, variants, thereof, like, king, piece, squ. An efficiently updatable neural network NNUE a Japanese wordplay on Nue sometimes stylised as ƎUII is a neural network based evaluation function whose inputs are piece square tables or variants thereof like the king piece square table 1 NNUE is used primarily for the leaf nodes of the alpha beta tree 2 While being slower than handcrafted evaluation functions NNUE does not suffer from the blindness beyond the current move problem 3 NNUE was invented by Yu Nasu and introduced to computer shogi in 2018 4 5 On 6 August 2020 NNUE was for the first time ported to a chess engine Stockfish 12 6 7 Since 2021 all of the top rated classical chess engines such as Komodo Dragon have an NNUE implementation to remain competitive NNUE runs efficiently on central processing units without a requirement for a graphics processing unit GPU 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 W1 encoded the king s position and therefore this layer needed only to be re evaluated once the king moved It used incremental computation and single instruction multiple data SIMD techniques along with appropriate intrinsic instructions 4 See also editelmo shogi engine Stockfish chess engine The chapter about NNUE features a visualization of NNUE List of chess softwareReferences edit Gary Linscott April 30 2021 NNUE GitHub Retrieved December 12 2020 Stockfish 12 Stockfish Blog Retrieved 19 October 2020 Stockfish Chessprogramming wiki www chessprogramming org Retrieved 2020 08 18 a b Yu Nasu April 28 2018 Efficiently Updatable Neural Network based Evaluation Function for computer Shogi PDF in Japanese Yu Nasu April 28 2018 Efficiently Updatable Neural Network based Evaluation Function for computer Shogi Unofficial English Translation PDF GitHub Introducing NNUE Evaluation 6 August 2020 Joost VandeVondele July 25 2020 official stockfish Stockfish NNUE merge GitHub External links editNNUE on the Chess Programming Wiki NNUE evaluation functions for computer shogi on github com Retrieved from https en wikipedia org w index php title Efficiently updatable neural network amp oldid 1222132973, wikipedia, wiki, book, books, library,
