awesome-machine-learning-in-compil
wcc
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| awesome-machine-learning-in-compil | wcc | |
|---|---|---|
| 1 | 1 | |
| - | 4 | |
| - | - | |
| - | 0.0 | |
| - | about 5 years ago | |
| C | ||
| - | - |
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awesome-machine-learning-in-compil
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I spent 5 years writing my own operating system
The list goes on.
Genode, unikernels like MirageOS, TempleOS, Singularity OS / Sing#, compiler services like Roslyn and Kotlin, MILEPOST GCC, Tensorflow / TPUs, GPT-3, all of the machine learning in compilers [1] and so much more. I truly think Deep Learning Compilers will be huge.
[1] https://github.com/zwang4/awesome-machine-learning-in-compil...
wcc
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I spent 5 years writing my own operating system
It's certainly plausible
It's currently using wcc to compile the kernel https://github.com/wandwramp/wcc. We can swap it with gcc for sure, but require a lot of work, especially the output format and backend.
Winix uses a simple header for binaries, https://github.com/halfer53/winix/blob/master/include/winix/..., so we would need to tweak gcc to support this header format and =add backend support for WRAMP architecture.
ELF is quite complicated, if I have to do this, I probably just copy some codes to linux.
What are some alternatives?
ZenithOS - The Zenith Operating System is a modernized, professional fork of the 64-bit Temple Operating System.
winix - A UNIX-style Operating System for the Waikato RISC Architecture Microprocessor (WRAMP)
rexsimulator - a forked copy of https://sourceforge.net/projects/rexsimulator/