Power Reduction in CNN Pooling Layers with a Preliminary Partial Computation Strategy
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Bibliographic record
Abstract
Convolutional neural networks (CNNs) are responsible for many recent successes in the computer vision field and are now the dominant approach for image classification. However, CNN-based methods perform many convolution operations and have high power consumption which makes them difficult to deploy on mobile devices. In this paper, we propose a new method to reduce CNN power consumption by simplifying computations before max-pooling layers. The proposed method estimates the output of the max-pooling layer by approximating the preceding convolutional layer with a preliminary partial computation. Then, the method performs a complementary computation to generate an exact convolution output only for the selected feature. We also present an analysis of the approximation parameters. Simulation results show that the proposed method reduces the power consumption by 21% and the silicon area by 19% with negligible degradation in classification accuracy for the CIFAR-10 dataset.
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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 | 0.001 |
| 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