Improving Energy-Efficiency of Capsule Networks on Modern GPUs
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Bibliographic record
Abstract
Convolutional neural networks (CNNs) have become the compelling solution in machine learning applications as they surpass human-level accuracy in a certain set of tasks. Despite the success of CNNs, they classify images based on the identification of specific features, ignoring the spatial relationships between different features due to the pooling layer. The capsule network (CapsNet) architecture proposed by Google Brain's team is an attempt to address this drawback by grouping several neurons into a single capsule and learning the spatial correlations between different input features. Thus, the CapsNet identifies not only the presence of a feature but also its relationship with other features. However, the success of the CapsNet comes at the cost of underutilization of resources when it is run on a modern GPU equipped with tensor cores (TCs). Due to the structure of capsules in the CapsNet, quite often, functional units in a TC are underutilized which prolong the execution of capsule layers and increase energy consumption. In this work, we propose an architecture to eliminate ineffectual operations and improve energy-efficiency of GPUs. Experimental measurements over a set of state-of-the-art datasets show that the proposed approach improves energy-efficiency by 15% while maintaining the accuracy of CapsNets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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