Boosting Entropy with Bell Box Quantization
Quantization-Aware Pre-Training (QAPT) is an effective technique to reduce the compute and memory overhead of Deep Neural Networks while improving their energy efficiency on edge devices. Existing QAPT methods produce models stored in compute-efficient data types (e.g. integers) that are not information theoretically optimal (ITO). On the other hand, existing ITO data types (e.g. Quantile/NormalFloat Quantization) are not compute-efficient. We propose BBQ, the first ITO quantization method that is also compute-efficient. BBQ builds on our key insight that since learning is domain-agnostic, the output of a quantizer does not need to reside in the same domain as its input. BBQ performs ITO ...
| Authors | Ningfeng Yang, Tor M. Aamodt |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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