Efficient Approximate Posit Multipliers for Deep Learning Computation
Bibliographic record
Abstract
Posit numeric format is getting more and more attention in recent years. Its tapered precision makes it especially suitable in many applications including deep learning computation. However, due to its dynamic component bit-width, the cost of implementing posit arithmetic in hardware is more expensive than its floating-point counterpart. To solve this cost problem, in this paper, posit multipliers with approximate computing features are proposed. The core idea of the proposed design is to truncate the fraction multiplier according to the estimated fraction bit-width of the product. So that the resource consumption of the fraction multiplier and thus the fraction adder can be significantly reduced. The proposed method is applied in both linear domain and logarithm domain posit multipliers. The 8/16/32-bit version of the proposed approximate posit multipliers are implemented and analyzed. For the commonly used 16-bit posit format in deep learning computation, the proposed approximate posit multiplier can consume 16% less power compared to the conventional posit multiplier design. The proposed 16-bit approximate logarithm multiplier can achieve a 15% improvement in terms of power consumption compared to the state-of-the-art posit approximate logarithm multiplier. The proposed 16-bit approximate posit multipliers are applied in the computation of several deep neural network models and significant improvements on energy efficiency can be achieved with negligible accuracy degradation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".