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Readme updated

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Nicolas 2026-03-04 15:16:51 +01:00
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README.md
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@ -13,7 +13,7 @@ The main features can be summarized as:
- Memory and type safety with a minimal set of runtime errors
- Deterministic execution
- 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
- 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:
- 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
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.
[^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.