[Numpy-discussion] ENH: Proposal to add KML_BLAS support

[ChunLin Fang]

  Hi all,
    Whether you're running apps on your phone or the world's fastest
supercomputer, you're most likely running ARM. Many major events have
occurred related to ARM archtecture:

   - Apple may have done the most to make ARM relatively relevant in
   popular culture with its new ARM-based M1 processor.
   - Amazon Web Services launched its Graviton2 processors based on the Arm
   architecture , which promise up to 40% better performance from comparable
   x86-based instances for 20% less.
   - Microsoft currently uses Arm-based chips from Qualcomm in some of its
   Surface PCs.
   - Huawei unveiled a new chipset called the Kunpeng based on ARM,
   designed to go into its new TaiShan servers, in a bid to boost its nascent
   cloud business.

     So It's obvious that ARM will become more and more popular in the
future, Since Intel MKL has provide good accelerate support for X86-based
chips, Huawei also published KML_BLAS
<https://kunpeng.huawei.com/en/#/developer/devkit/library>(kunpeng math
library blas) that can make full advantage of ARM-based chips,  KML_BLAS is
a mathematical library for basic linear algebra operations. it provides
three levels of high-performance vector operations: vector-vector
operations, vector-matrix operations, and matrix-matrix operations. The
performance advantage is shown in the attachment compared with OpenBlas.
Can we add KML_BLAS support to numpy?

Cheers,
Chunlin Fang(github ID:Qiyu8)
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