JPEG-ACT: Accelerating Deep Learning via Transform-based Lossy Compression
Bibliographic record
Abstract
A reduction in the time it takes to train machine learning models can be translated into improvements in accuracy. important factor that increases training time in deep neural networks (DNNs) is the need to store large amounts of temporary data during the back-propagation algorithm. To enable training very large models this temporary data can be offloaded from limited size GPU memory to CPU memory but this data movement incurs large performance overheads. We observe that in one important class of DNNs, convolutional neural networks (CNNs), there is spatial correlation in these temporary values. We propose JPEG for ACTivations (JPEGACT), a lossy activation offload accelerator for training CNNs that works by discarding redundant spatial information. JPEGACT adapts the well-known JPEG algorithm from 2D image compression to activation compression. We show how to optimize the JPEG algorithm so as to ensure convergence and maintain accuracy during training. JPEG-ACT achieves 2.4× higher training performance compared to prior offload accelerators, and 1.6× compared to prior activation compression methods. An efficient hardware implementation allows JPEG-ACT to consume less than 1% of the power and area of a modern GPU.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".