Synchronized-transfer-level design methodology applied to hardware matrix multiplication
Bibliographic record
Abstract
In an effort to reduce the productivity gap separating hardware design and software programming practices, this paper presents the application of our synchronized-transfer-level hardware design methodology to the implementation of a hardware matrix multiplication accelerator. The methodology builds on a hardware description language for which the designer manages dynamic connections between sources and sinks that may not always be ready to send or receive data tokens. In addition to these connections, the designer can constrain the authorization of data transfers by the means of logical rules that make transfers dependant on each other. Combining both finite state machine and constraint programming paradigms, the featured description language enhances the ability to express and exploit low-level parallelism. A compiler automates the generation and the optimization of the synchronization logic, whose low-level complexity is thus hidden to the designer. Applied to the design of the pipelined matrix multiplication circuit, the proposed methodology leads to similar computing performances than the dedicated designs reported in the literature but within shorter design times (a single day), simpler source code and no need for advanced hardware design skills.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".