Bibliographic record
Abstract
NVIDIA introduced a new generation of graphics processing unit (GPU), called Volta, to meet the growing demand for higher performance in Deep Neural Networks (DNNs). Volta GPUs exploit a dedicated hardware unit, called Tensor Core (TC), to accelerate multiplication of matrices. While TCs offer significant computational horsepower for deep learning applications, they increase the power budget of DNNs. In this work, we exploit error resiliency property of DNNs and propose a technique to reduce energy of TCs. In particular, we propose an approximate architecture with the flexibility of switching between exact and approximate operating modes. In approximate mode, TCs offer significant energy saving at the cost of lower accuracy. To mitigate the impact of approximation on accuracy, we propose a mixed-precision architecture where a combination of exact and approximate units is used to reduce error in DNNs. The mixed-precision architecture provides the flexibility for the software stack to tune the level of approximation to satisfy a desired inference accuracy in DNNs. We evaluate the proposed architecture using DNNs selected from a wide range of application domains and show that the mixed-precision architecture achieves 40% energy saving while maintaining accuracy of DNNs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".