Bibliographic record
Abstract
Convolution is an operation crucial to Deep Learning applications.As such, it has been the focus of many optimization efforts on this application domain.Image-to-column (Im2col) and column-to-image (Col2im) are data transformations extensively used to map convolution to matrix multiplication.These transformations rearrange the inputs of convolution to avoid its strided memory access pattern, thus providing a friendlier data layout for CPUs and GPUs.In artificial intelligence (AI) accelerators, these transformations allow convolution to be computed in matrix-multiplier units.Implemented in software, however, they impose a significant overhead that must be compensated by the efficiency gains of matrix-multipliers.DaVinci is an AI accelerator architecture that introduces instructions to optimize Im2col and Col2im, thus lowering the overhead of executing convolution in its matrix-multiplier.Another core layer of convolutional neural networks that presents a similar memory access pattern to convolution is pooling.The execution of pooling is typically targeted to vector computational units.Nevertheless, implementations based on the Im2col and Col2im transformations can be leveraged to improve its execution.This work explores the use of Im2col and Col2im instructions of DaVinci to accelerate pooling layers.The proposed approach uses a general-purpose vector computational unit and instructions primarily designed for convolution.An experimental evaluation reveals that the proposed pooling implementations can yield up to 5.8x speedup compared to baseline implementations that do not use these specialized instructions.The speedups follow from an improved memory layout in the inputs of pooling, as this layout leads to better vectorization of its instructions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".