Algebraic Enhancements for Systolic Arrays
Bibliographic record
Abstract
The field of deep learning has seen increasing breakthroughs and commercial adoption in recent years for enabling a wide range of applications including image and speech recognition, multimedia generation, information summarization, and human-like chatbots. This has led to a growing need for hardware that can quickly and efficiently perform deep learning inference, which increasingly requires massive amounts of computational power. To address this need, recent years have seen many works for optimizing deep learning inference in hardware. Systolic arrays are an efficient class of hardware designs to use as a starting point for this application. However, after hardware-oriented deep learning model optimizations reach their limits, after the known parallelism for executing their compute patterns in hardware is exhausted, and after technology scaling slows to a halt, there is an accelerator wall that limits further improvement on the implementation side. In this thesis, we contribute to this field through an under-explored direction by presenting new efficient matrix multiplication algorithms and/or their systolic-array hardware architectures that increase performance-per-area by reducing the workload at the algebraic level, and thus by computing the same result from a re-arranged compute pattern requiring fewer or cheaper operations to be performed in hardware. We evaluate our architectures in an end-to-end deep learning accelerator, demonstrating their ability to increase the performance-per-area of hardware accelerators beyond their normal theoretical limits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".