UniPipe puts serving and fine-tuning in one CUDA context: on a ptychography stream its miss rate tracks an inference-only deployment
For scientific workflows that must process sensor data within hard deadlines, UniPipe runs inference serving and online fine-tuning of a deep-neural-network surrogate in a single CUDA context on one edge GPU, handling inference requests and labeled samples in one forward pass and backpropagating only through the labeled subset. On a real ptychographic reconstruction workflow with PtychoNN it lowers inference and reconstruction MSE over a frozen pretrained baseline, reduces the deadline-miss rate over a two-context concurrent baseline, and nearly halves the 95th-percentile latency spread relative to the mean.