Decision Tree-based Adaptive Approximate Accelerators for Enhanced Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hardware accelerators are used for parallel computation with the tendency to accept inexact results. Such accelerators are used extensively in big-data processing applications, and thus can be designed approximately for reduced power consumption, area and processing time. However, since for some inputs the output errors may reach unacceptable levels, the main challenge is to ensure the quality of the approximated results. Towards this goal, in this paper, we propose a fine-grained input-dependent decision tree-based adaptive approximate design to meet the output quality constraints set by the user. For illustration purposes, we use a library of 16-bit approximate array multipliers with 20 different settings. The proposed methodology has been evaluated for audio and image processing applications. The simulation result, demonstrate the effectiveness of the proposed methodology, utilizing a lightweight decision tree-based design selector where the proposed adaptive design achieves the userspecified target output quality with a 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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