- Source: Efficiently updatable neural network
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. NNUE is used primarily for the leaf nodes of the alpha–beta tree. While being slower than handcrafted evaluation functions, NNUE does not suffer from the 'blindness beyond the current move' problem.
NNUE was invented by Yu Nasu and introduced to computer shogi in 2018. On 6 August 2020, NNUE was for the first time ported to a chess engine, Stockfish 12. Since 2021, many 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 (CPU) without a requirement for a graphics processing unit (GPU). In contrast, deep neural network-based chess engines such as Leela Chess Zero require a 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.
See also
elmo (shogi engine)
Stockfish chess engine - The chapter about NNUE features a visualization of NNUE.
List of chess software
References
External links
NNUE on the Chess Programming Wiki.
NNUE evaluation functions for computer shogi on github.com
Kata Kunci Pencarian:
- Stockfish
- Efficiently updatable neural network
- Evaluation function
- Computer chess
- Stockfish (chess)
- Fritz (chess)
- Neural network (machine learning)
- Recurrent neural network
- Convolutional neural network
- Rectifier (neural networks)
- Evaluation
