Distributed Neural Network Computation for Resource-Limited Robotics

The patent divides the neural network computation into separate forward propagation and backward propagation phases that can be executed independently and interleaved with other tasks. This segmentation allows resource-constrained systems to perform computation in manageable intervals rather than requiring continuous dedicated resources, thereby improving resource utilization while maintaining learning efficiency through systematic parameter updates.

The patent implements periodic computation cycles where forward propagation, error calculation, and parameter updates are performed in alternating intervals rather than continuously. This periodic approach allows the system to interleave neural network training with other computational tasks, improving overall resource utilization while ensuring that learning occurs at regular intervals to maintain efficiency.

The patent calculates and stores intermediate values during the forward propagation phase that are later reused in the backward propagation phase. By performing preliminary computations and storing necessary intermediate results, the system avoids redundant calculations during backpropagation, thereby reducing overall computational complexity while maintaining the learning efficiency benefits of gradient-based optimization.

The patent creates and maintains copies of intermediate activation values and computational states from the forward pass that are needed for the backward pass. By copying and storing these intermediate results rather than recalculating them, the system reduces the computational burden of backpropagation while preserving the ability to efficiently compute gradients and update parameters.