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@github-actions github-actions released this 10 Aug 18:55
· 95 commits to master since this release
v5.3.0
9d2f48b

This releases comes with a new neural network, search improvements and some small fixes

Estimated strength increase: ~ 25 Elo

Changes

  • New neural network trained from v5.2.0 training set + re-scored training sets from earlier Velvet versions
  • Updated training tools
  • Some minor search improvements and fixes
  • Some optimizations

Statistics

  • Elo change: v5.3.0 compared to v5.2.0 against the same set of opponents
  • Move range: grouped by games won in less than x moves (each game only belongs to one group, so a game that ended in 57 moves would belong to the group "60", but not "80", "100", etc.)
Move range Elo change
40 +24
60 +32
80 +22
100 +31
120 +37
>= 120 +12

Notes

Due to the lack of an ARM-based (Apple Silicon) computer, the new "apple-silicon" builds are untested.

Installation

The chess engine is available for Windows and Linux and requires a 64 Bit CPU.
There are optimized executables available for different CPU micro-architecture generations.

Starting with Velvet v4.1.0 there are also builds for macOS provided.
Currently there are no specific optimizations for the ARM-based/Apple Silicon builds implemented, so
the macOS builds for x86_64 might be faster.

If you have a relatively modern CPU (2013+) with AVX2 support, then the ...-x86_64-avx2 executable is highly recommended for best performance.

Executable Description Min. CPU Generation Required Instruction Sets
x86_64-avx2 Recommended for best performance on a modern CPU Intel Haswell / Zen1 AVX2, BMI1
x86_64-sse4-popcnt Lower performance, recommended for CPUs without AVX2 support Intel Nehalem / AMD Bulldozer SSE4.2, SSE3, POPCNT
x86_64-nopopcnt Lowest performance, but compatible with most x86_64 CPUs --- SSE2, CMOV
apple-silicon Native builds for Apple Silicon processors (ARM aarch64) Apple M1
Assets 13