Hi, instead of JNA, I would be happy to provide bindings for the C API and maintain packages based on the JavaCPP Presets here: https://github.com/bytedeco/javacpp-presets/tree/master/mxnet JavaCPP adds no overhead, unlike JNA, and is often faster than manually written JNI. Plus JavaCPP provides more tools than JNA to automate the process of parsing header files as well as packaging native libraries in JAR files. I have been maintaining modules for TensorFlow based on JavaCPP, and we actually got a boost in performance when compared to the original JNI code: https://github.com/tensorflow/java/pull/18#issuecomment-579600568 I would be able to do the same for MXNet and maintain the result in a repository of your choice. Let me know if this sounds interesting! BTW, the developers of DJL also seem opened to switch from JNA to JavaCPP even though it is not a huge priority. Still, standardizing how native bindings are created and loaded with other libraries for which JavaCPP is pretty much already the standard (such as OpenCV, TensorFlow, CUDA, FFmpeg, LLVM, Tesseract) could go a long way in alleviating concerns of stability.
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