any-precision networks

**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.