Moku Neural Network

A neural network is a machine learning structure inspired by the human brain. It consists of interconnected units called neurons, grouped together in layers: an input layer, one or more hidden (internal) layers, and an output layer. In a fully connected model like the Moku Neural Network, each neuron has a series of weights indicating how much the value or any neuron in the previous layer affects its output. These weights are adjusted during a “training” phase so the values at the output layer match a known set of corresponding inputs. After training, the model can be used for “inference,” to infer a set of output values given some inputs that it may or may not have seen before.

Neural networks are typically built and run on combinations of CPUs and/or GPUs. This approach gives incredible computing power, but it is also resource-intensive. Large AI models are energy hungry and often excessive for many types of signal processing applications. The flexibility and real-time processing of FPGAs makes them strong candidates for implementing small-scale neural networks. Their parallel processing capabilities benefit the linear algebra and other complex mathematics involved in the propagation of information through the network.

To train a model for the Moku Neural Network, follow this example. It walks you through how to configure your model, generate training data, train the model, and export the resulting weights and biases as a .linn model to be uploaded to the Moku Neural Network.

Not at all — this versatile instrument is engineered to be accessible to all researchers. While machine learning experts can take Moku Neural Network applications to the next level, it’s easy for all Moku:Delta or Moku:Pro users to set up a neural network.