nicolas
/
copapy
mirror of https://github.com/Nonannet/copapy.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Readme updated

This commit is contained in:
Nicolas 2026-03-04 15:16:51 +01:00
parent 031249241e
commit 01f02cc9ba
1 changed files with 2 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
README.md
View File

@ -13,7 +13,7 @@ The main features can be summarized as:
- Memory and type safety with a minimal set of runtime errors - Memory and type safety with a minimal set of runtime errors
- Deterministic execution - Deterministic execution
- Automatic differentiation for efficient realtime optimization (reverse-mode) - Automatic differentiation for efficient realtime optimization (reverse-mode)
- Optimized machine code for x86_64, ARMv6, ARMv7 and AArch64 - Optimized machine code for x86_64, 32 Bit ARM (Cortex-A and Cortex-M) and AArch64
- Highly portable to new architectures - Highly portable to new architectures
- Small Python package with minimal dependencies and no cross-compile toolchain required - Small Python package with minimal dependencies and no cross-compile toolchain required
@ -31,7 +31,6 @@ While hardware I/O is obviously a core aspect of the project, it is not yet avai
Currently in development: Currently in development:
- Array stencils for handling very large arrays and generating SIMD-optimized code - e.g., for machine vision and neural network applications - Array stencils for handling very large arrays and generating SIMD-optimized code - e.g., for machine vision and neural network applications
- Support for Thumb instructions required by ARM*-M targets (for MCUs)
- Constant regrouping for further symbolic optimization of the computation graph - Constant regrouping for further symbolic optimization of the computation graph
Despite missing SIMD-optimization, benchmark performance shows promising numbers. The following chart plots the results in comparison to NumPy 2.3.5: Despite missing SIMD-optimization, benchmark performance shows promising numbers. The following chart plots the results in comparison to NumPy 2.3.5:
@ -253,4 +252,4 @@ This project is licensed under the MIT license - see the [LICENSE](LICENSE) file
[^2]: The compiler must support tail-call optimization (TCO). Currently, GCC is supported. Porting to a new architecture requires implementing a subset of relocation types used by that architecture. [^2]: The compiler must support tail-call optimization (TCO). Currently, GCC is supported. Porting to a new architecture requires implementing a subset of relocation types used by that architecture.
[^3]: Supported architectures: x86_64, AArch64, ARMv6 and 7 (non-Thumb). ARMv6/7-M (Thumb) support is in development. Code for x86 32-bit exists but has unresolved issues and a low priority. [^3]: Supported architectures: x86_64, AArch64, ARMv6/7 (non-Thumb) and ARMv7 Thumb for Cortex-A and Cortex-M. Code for x86 32-bit exists but has unresolved issues and a low priority.