One of the developers behind Apple’s QuickTime and ex Cornell Physics educated, Maynard Handley, recently shared a 350 page PDF analysing and explaining the inner workings of the infamous M1 ARM chip.
The document, currently published as version 0.7, dives deep into M1’s architecture from a reverse-engineering perspective and is heavily being fed by other professionals and enthusiasts in the field.
The researcher’s PDF goes into excruciating details on the technical elements of the processor, and it can easily be used by developers and budding engineers to reverse-engineer the chip. It has contributions from the team that ran Linux on the M1 and contributions from independent testers. The document even includes instructions on how to run your own testing.
Apple’s M1 chip stunned the world of technology with its incredible IPC and power-to-performance ratio, shifting the spotlight from x86 as the new high-performance architecture. But, Apple’s walled-garden approach to its tech only ensures that anyone who wants to take advantage of the M1 hardware has to either go through Apple’s macOS or follow the only remaining option of a reverse engineering route optimising another software stack to use on the Apple silicon. This also means that we could potentially see hyper-efficient and performance heavy ARM CPUs for Linux and even for Windows in future.
A project like this could lead to an open-source M1-type chip, which could unlock all new paths for independent engineers and startup teams. An open-source chip would make it only easier to manufacture competing hardware, from ARM-based laptops all the way to iPad Pro-level tablets.
It is still unclear if Apple agrees with this information being readily available for free on the Internet. Although the document is nowhere near a definitive version and yet to reach full completion as there are still some unknown inner workings, more and more people from the community are contributing to it every day.
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Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.