ML-based Load Value Approximator for Efficient Multimedia Processing
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
Approximate computing (AC) has gained traction as an alternative computing method for energy-efficient processing. This article proposes the exploitation of AC to address the memory wall. The proposed model predicts the memory load value using machine learning (ML). Subsequently, the ML model is a load value approximator (LVA) where the generated value is accepted as-is. The proposed LVA was tested under various approximate conditions, where 50% to 95% of the load instructions were approximated using a set of multimedia applications. The memory access operation using the proposed LVA was more than \(6\times\) faster in multiple cases. Additionally, the applications tested ran on average \(1.83\times\) faster. The peak signal-to-noise ratio (PSNR) exceeded 37 dB in several scenarios. The average normalized mean absolute error (NMAE) was 4.54%.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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