Synaptic Array Pipelining for Analog Neural Network Training

The patent divides the neural network processing into separate stages (forward propagation, backward propagation, weight update) that can be pipelined. Each stage processes data independently, allowing intermediate results to be stored temporarily in small local buffers rather than requiring large memory for all data simultaneously. This segmentation enables accurate weight updates while reducing overall memory requirements.

The patent performs preliminary computation of forward propagation results and stores them in local buffers before weight updates are needed. This preliminary action allows the system to have accurate weight update data ready without requiring large amounts of memory to hold all intermediate results indefinitely, as they are computed and stored in advance in compact local storage.

The patent segments the memory requirements into small local buffers at each layer rather than requiring large centralized memory. This segmentation allows the system to pipeline operations efficiently within each layer while maintaining scalability, as each layer independently manages its own small buffer rather than requiring proportional increases in total memory for larger networks.

The patent transitions from a centralized memory architecture to a distributed local buffer architecture across multiple layers. This dimensional change in memory organization enables pipelining capability by providing local storage at each stage, while simultaneously improving scalability by allowing the network to grow without requiring proportional increases in any single memory component.