The Secret Apple M1 AMX: Apple Matrix Coprocessor #ReverseEngineering #M1 #Apple @Apple @erikengheim

Erik Engheim posts on Medium about Developer Dougall Johnson thoroughly reverse engineering the Apple M1 processor, uncovering a secret powerful coprocessor dubbed AMX: Apple Matrix coprocessor.

A matrix is basically just a table of numbers. If you have worked with spreadsheets such as Microsoft Excel, you have basically worked with something very similar to matrices. The key difference is that in math such tables of numbers have a laundry list of operations they support and specific behavior.

The reason matrices are important is because they are heavily used in:

  • Image processing
  • Machine learning
  • Speech and handwriting recognition
  • Face recognition
  • Compression
  • Multimedia: audio and video

In particular machine learning which has been hot these last years. Just adding more cores to the CPU will not make this run fast enough as it is really demanding. You really need specialized hardware. Regular tasks such browsing the internet, writing email, word processing and spreadsheets has been running fast enough for years. It is for specialized tasks which we really need to boost the processing power.

Why Is the AMX a Secret?

Yet Apple has indeed extended their ARM CPU cores with AMX instructions. How do we know? Apple has kept this a secret. There is no manual, publicly available, describing these instructions. However developer Dougall Johnson has done an amazing reverse engineering effort on the M1 to discover this coprocessor. His efforts are described here. For matrix related math operations Apple has special libraries or frameworks such as Accelerate, which is made up of:

  • vImage — higher level image processing, such as converting between formats, image manipulation.
  • BLAS — a sort of industry standard for linear algebra (what we call the math dealing with matrices and vectors).
  • BNNS — is used for running neural networks and training.
  • vDSP  digital signal processing. Fourier transformations, convolution. These are mathematical operations important in image processing or any signal really including audio.
  • LAPACK — higher level linear algebra functions, e.g. for solving linear equations.

Johnson wrote special programs to analyze and observe what these programs did to discover the special undocumented AMX machine code instructions.

Read Engheim’s analysis here.

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