The community has long expressed interest in supporting Ada on ARM64 and Apple Silicon platforms. Unfortunately, GitHub Action Runners are not available for ARM for open-source projects. The publication of the Ada extension for VS Code (as well as Alire) relies on GitHub Actions, limiting ARM support. However, it turns out that creating an ARM version is still possible, and I’d like to share my experience with using cross-compilers for this purpose.

Through the actions described below, I managed to obtain two cross-compilers for the aarch64 CPU (running on x86_64), one for Linux and the other for macOS. These proved sufficient to build ALS and VSCode extensions for these platforms.

I began by building the Ada Language Server version for ARM64/Linux since this platform is more familiar to me. Ubuntu provides cross-versions of dependencies for GNAT (glibc, binutils). Ubuntu even has GNAT GCC 13, but on newer versions than the one (20.04) we use for ALS building. The first thing I did was attempt to rebuild GCC 13 on Ubuntu 20.04 from the source package of the latest Ubuntu distribution (23.10). Although this might be the most correct method from a reuse perspective, it didn’t suit me - too many packages and installation steps.

Building GNAT from the GCC 13 source texts proved much simpler. The resulting script was just a dozen lines long.

If you unpack the resulting archive in /, gprbuild finds the cross-compiler and using it subsequently is straightforward: you just need to specify the option --target=aarch64-linux.

Mac OS also provides cross-versions of binutils and libc, which come with the standard installation of Xcode Command Line Tools. For example, /usr/bin/as can create object files for both x86_64 and aarch64, depending on the --target. Therefore, we only need to build GNAT using the GCC sources from the repository iains/gcc-13-branch.

If you need binaries for these cross-compilers or have ideas on how to use them, please leave a comment in this thread.

PS Thanks to Simon Wright for experimenting with Mac OS and providing advices.

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As an Ada enthusiast, I’ve always wanted to try my hand at machine learning, but was discouraged by the fact that Python seemed to be the only language supported by most ML frameworks. However, I recently discovered the Open Neural Network Exchange (ONNX) format, a universal way of representing machine learning models, and a library for working with it. One thing I love about ONNX is that there are many pre-trained models available for various applications.

I was excited to find out that I could use Ada to load and make inference with ONNX models, so I created a binding for the ONNX Runtime library and wrote a couple of example programs. These examples include handwriting digit recognition and English speech synthesis. Unfortunately, the speech synthesis example required converting text to phoneme sequences, which I haven’t yet implemented in Ada, in order to keep the examples compact.

Overall, I’m thrilled to have been able to dive into the world of machine learning using my favorite programming language, and I hope that other Ada enthusiasts will find this information useful. If anyone is interested in trying it out for themselves, I’m happy to share my code and provide more information on how to get started with Ada and ONNX.

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