Bibliographic record
Abstract
The introduction of tensor core (TC) in NVIDIA GPUs has accelerated neural network computations. A TC is a set of storage and arithmetic units dedicated to handling matrix-multiply-and-accumulate (MMA) operations. While TC reduces the runtime of convolutional neural networks (CNNs), it increases power consumption. In particular, register files within TCs consume a significant fraction of leakage power as GPU designers steadily have increased the size of register files to boost performance. In this work, we propose a value-based approach to reduce leakage power in register files. We observe that register bits exhibit a strong bias towards zeros. We exploit this bit-level bias property and propose a low-power SRAM (LPS) cell that draws significantly less leakage current than regular SRAM cells. In the preferred state, the leakage power is smaller by as much as approximately 49×. We also propose LPS+ which increases the sparsity rate in SRAM cells by selectively inverting register bits. To reduce leakage power further, we propose precision-aware LPS+ (PLSP+) which exploits the error resiliency property of CNNs and drops low-order bits in network values. We evaluate our proposed techniques using state-of-the-art CNNs and show that leakage power is reduced by 77.3% with a negligible impact on accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.021 | 0.004 |
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".