📝 Overview

This project studies how to make fixed-point online learning stable when a deployed model must keep training after its input distribution changes.

The ENABOL paper frames the stability problem as a bounded-gain control problem. A trainable fixed-point network should not let activations, gradients, weights, or optimizer updates exceed the rails implied by the chosen ap_fixed formats. The proposed mechanism is kappa budgeting: assign per-layer induced-norm budgets and enforce them during training.