MATSORT: Fast and Efficient Approximate Matrix Sorting Algorithm for DNN Applications
Bibliographic record
Abstract
This paper proposes Matrix Sorting (MATSORT), a novel algorithm for compressing sparse matrices to enable efficient execution of sparse Multiply-Accumulate (MAC) operations in parallel computing architectures for Deep Neural Networks (DNNs). Pruning in DNNs often results in weight matrices with a large proportion of zero weights, leading to significant resource and time wastage when processed by parallel computing architecture. MATSORT outperforms state-of-the-art (SoTA) techniques in both compression speed and compression rate—two key factors for efficient sparse MAC operations. The algorithm employs an approximate sorting method and a novel merging strategy, reducing the sorting time complexity while resolving long-standing element conflict issues. These optimizations achieve nearly 100% compression rates across all tested matrices, substantially enhancing power and resource efficiency. Evaluation results show that MATSORT delivers a maximum speedup of 441× for a 1280 × 1280 matrix and a minimum speedup of 1.63× for a 110 × 128 matrix, outperforming existing methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".