BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Bibliographic record
Abstract
BitPruning is a training method for minimizing inference bitlengths at any granularity while maintaining accuracy. BitPruning extends the meaning of fixed-point bitlenghts into the continuous domain by interpolating between the nearest two integers, enabling gradient descent to learn bitlengths together with other parameters. A novel regularizer penalizes large bitlength representations and can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. BitPruning learns thrifty representations while maintaining accuracy: With ImageNet, it produces an average per layer bitlength of 3.76 and 4.36 bits on ResNet18 and MobileNet V2 respectively, remaining within 0.5% of the base TOP-1 accuracy. Simple modifications of the BitPruning regularizer can be used to further reduce compute workload by up to 24%, as well as memory footprint in activation or weight-heavy tasks by up to 14% and 8% respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".