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.[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]

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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] As of 2022, all of the top rated classical chess engines, such as Komodo, have an NNUE implementation to remain competitive.

NNUE runs efficiently on central processing units without a requirement for a graphics processing unit (GPU). Compared to deep neural network based evaluations requiring a dedicated GPUs, NNUE avoids idle times during the substantial data transfer operations between GPU and CPU required before and after each evaluation.

The neural network used for shogi consists of four weight layers: W1 (16-bit integers) and W2, W3 and W4 (8-bit). Incremental computation and single instruction multiple data (SIMD) techniques are used with appropriate intrinsic instructions, specifically in the 2018 computer shogi implementation VPADDW, VPSUBW, VPMADDUBSW, VPACKSSDW, VPACKSSWB and VPMAXSB.[4]