A Low-Power Scalable Stream Compute Accelerator for General Matrix Multiply (GEMM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many applications ranging from machine learning, image processing, and machine vision to optimization utilize matrix multiplication as a fundamental block. Matrix operations play an important role in determining the performance of such applications. This paper proposes a novel efficient, highly scalable hardware accelerator that is of equivalent performance to a 2 GHz quad core PC but can be used in low-power applications targeting embedded systems requiring high performance computation. Power, performance, and resource consumption are demonstrated on a fully-functional prototype. The proposed hardware accelerator is 36× more energy efficient per unit of computation compared to state-of-the-art Xeon processor of equal vintage and is 14× more efficient as a stand-alone platform with equivalent performance. An important comparison between simulated system estimates and real system performance is carried out.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it