Learning the Error Features of Approximate Multipliers for Neural Network Applications
Why this work is in the frame
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Bibliographic record
Abstract
Approximate multipliers (AMs) have widely been investigated to pursue high-performance and energy-efficient hardware designs for error-tolerant applications, such as neural networks (NNs). The computing accuracy of an AM has been evaluated by using statistical error features; however, it is difficult to estimate the quality of a specific application using AMs. Thus, it is a great challenge to select or design appropriate AMs for an accuracy-constrained application. This paper proposes an application-oriented error evaluation framework for AMs with the aim of exploring the correlation between statistical error features of AMs and the accuracy degradation in AM-based NN applications. Specifically, based on the Dropout Feature Ranking technique, statistical error features of AMs are extensively studied and ranked by their importance to the accuracy of AM-based NN applications. The three most informative features are obtained to construct error models to predict the accuracy loss of AM-based NN applications. The constructed classification models show a probability higher than 96% for correctly classifying the AMs into three categories in accordance with the induced accuracy loss in AM-based NN applications. Furthermore, regression models can predict the accuracy of NN applications using an AM with a deviation as low as 6%. These results show that the proposed error evaluation framework can guide an efficient selection of AMs for NN applications by using just several AM error features, instead of running time-consuming and complicated hardware simulation. The obtained statistical error features can also provide a guidance for the design or generation of application-oriented AMs. Moreover, the proposed framework is applicable for quickly analyzing and selecting other approximate circuits for error-tolerant applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it