The project presents a scheduling algorithm for neuron level parallelism. It implements a neural network training process performing forward and backward propagation, updating weights, and training the network using the backpropagation algorithm with stochastic gradient descent. It emphasizing performance optimization through asynchronous computation and memory usage monitoring