Neural Networks Without Matrix Math

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. This result is compared to the known "correct" answer, and the difference between the two is used to adjust the weights applied to the network nodes. The process repeats for as many training examples as needed to (hopefully) converge to a stable set of weights that gives acceptable accuracy. This standard algorithm requires two distinct computational paths -- a forward "inference" path to analyze the data, and a backward "gradient descent" path to correct node weights.