Machine-Learning-Based Self-Tunable Design of Approximate Computing
Why this work is in the frame
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Bibliographic record
Abstract
Approximate computing (AC) is an emerging computing paradigm suitable for intrinsic error-tolerant applications to reduce energy consumption and execution time. Different approximate techniques and designs, at both hardware and software levels, have been proposed and demonstrated the effectiveness of relaxing the average output quality constraint. However, the output quality of AC is highly input-dependent, i.e., for some input data, the output errors may reach unacceptable levels. Therefore, there is a dire need for an input-dependent tunable approximate design. With this motivation, in this article, we propose a lightweight and efficient machine-learning-based approach to build an input-aware design selector, i.e., quality controller, to adapt the approximate design in order to meet the target output quality (TOQ). For illustration purposes, we use a library of 8-bit and 16-bit energy-efficient approximate array multipliers with 20 different settings, which are commonly used in image and audio processing applications. The simulation results, based on two sets of images, including an 8 Scene Categories Dataset, which is a benchmark of images data set, demonstrate the effectiveness of the lightweight selector where the proposed tunable design achieves a significant reduction in quality loss with relatively low overhead.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it