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Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.