Noise-aware training
Beginner
Training an AI model with random errors added on purpose. Then it still works on chips that aren’t perfect.
Novice
Adding random perturbations, shaped like the hardware’s real errors, to the weights during training. The network learns settings that are less sensitive to those errors.
Expert
Hardware-aware training injects noise drawn from measured device statistics, optionally with quantization and ADC models in the loop; chip-in-the-loop fine-tuning then absorbs residual errors layer by layer.
Explained in Sparsity, in-memory and analog compute (Architectures).
See also: Analog compute.