werunplugged/unplugged-vendor
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
main
unplugged-vendor/external/ruy
History
ziv-gabel ebc759d833 Initial commit: AOSP 12 vendor with modifications for Unplugged OS
2025-10-06 13:59:42 +00:00
..
cmake Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
doc Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
example Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
ruy Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
third_party Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
.gitignore Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
.gitmodules Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
Android.bp Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
BUILD Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
CMakeLists.txt Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
CONTRIBUTING.md Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
LICENSE Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
README.md Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00
WORKSPACE Initial commit: AOSP 12 vendor with modifications for Unplugged OS 2025-10-06 13:59:42 +00:00

README.md

The ruy matrix multiplication library

This is not an officially supported Google product.

ruy is a matrix multiplication library. Its focus is to cover the matrix multiplication needs of neural network inference engines. Its initial user has been TensorFlow Lite, where it is used by default on the ARM CPU architecture.

ruy supports both floating-point and 8bit-integer-quantized matrices.

Efficiency

ruy is designed to achieve high performance not just on very large sizes, as is the focus of many established libraries, but on whatever are the actual sizes and shapes of matrices most critical in current TensorFlow Lite applications. This often means quite small sizes, e.g. 100x100 or even 50x50, and all sorts of rectangular shapes. It's not as fast as completely specialized code for each shape, but it aims to offer a good compromise of speed across all shapes and a small binary size.

Documentation

Some documentation will eventually be available in the doc/ directory, see doc/README.md.