Using Machine Learning for Quality Configurable Approximate Computing
Bibliographic record
Abstract
Approximate computing (AC) is a nascent energy-efficient computing paradigm for error-resilient applications. However, the quality control of AC is quite challenging due to its input-dependent nature. Existing solutions fail to address fine-grained input-dependent controlled approximation. In this paper, we propose an input-aware machine learning based approach for the quality control of AC. For illustration purposes, we use 20 configurations of 8-bit approximate multipliers. We evaluate these designs for all combinations of possible input data. Then, we use machine learning algorithms to efficiently make predictive decisions for the quality control of the target approximate application, based on experimentally collected training data. The key benefits of the proposed approach include: (1) fine-grained input-dependent approximation, (2) no missed approximation opportunities, (3) no rollback recovery overhead, (4) applicable to any approximate computation with error-tolerant components, and (5) flexibility in adapting various error metrics.
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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 | 0.001 | 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".