Efficient implementation of matrix multiplication ARM cortex A9 - Xilinx SDK

Question

Is there any simple way-library to efficient (max possible speed) implement linear algebra on an ARM CortexA9 dual core using Xilinx SDK?

I am using a zybo z7 developememt board with a dual core Arm proccesor and i want to implement a simple neural network with one convolution layer followed by a dense one, on Xilinx SDK. Specificaly, to tranfer a python numpy based model on Arm. I read some manuals for ARM and SIMD library but i don't want to dive so deep.

An easy way for me is to use a library and do the multiplication/dot product/convolve etc by itself (fast) like numpy in python and avoid pure for...loop syntax. An example would be nice!

Thank for your time

If you are asking for a recommendation on a library, you should ask elsewhere. — Jake 'Alquimista' LEE, Mar 11 '21 at 12:47

score 0 · Accepted Answer · answered Mar 09 '21 at 08:27

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You can try the Eigen library used by Tensorflow to implement the matrix calculations, or you can even try to use TensorFlow lite which is already tested with the ARM-Cortex M series of processors.

answered Mar 09 '21 at 08:27

jordanvrtanoski

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Thanks a lot! It works with high performane – Thodoris Barbakos Mar 11 '21 at 15:06

Efficient implementation of matrix multiplication ARM cortex A9 - Xilinx SDK

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