Any-Precision Networks are neural networks that can execute at any bit-width precision at runtime — a single trained model supports inference at full precision (32-bit), reduced precision (8-bit, 4-bit), or even binary (1-bit), with the precision selected based on the available hardware or accuracy requirements.

Any-Precision Training

• Shared Weights: The same weight values are quantized to different precisions — higher bits extract more information from the same weights.
• Joint Training: Train at all precision levels simultaneously — weights are optimized to perform well at every precision.
• Knowledge Distillation: Higher precision acts as teacher for lower precision during training.
• Precision Selection: At runtime, choose precision based on hardware capability, latency budget, or accuracy needs.

Why It Matters

• Flexible Deployment: One model works on any hardware — from powerful GPUs (32-bit) to tiny MCUs (4-bit or 1-bit).
• Single Storage: Store one model instead of separate models for each precision level.
• Adaptive: Dynamically switch precision based on runtime conditions (battery level, thermal throttling).

Any-Precision Networks are one model, any precision — supporting runtime-selectable bit-widths for flexible deployment across diverse hardware.