Hardware description and synthesis of control-intensive reconfigurable dataflow architectures (abstract only)
Bibliographic record
Abstract
Field-Programmable-Gate-Arrays are used increasingly to speed up applications in various fields of science. But as modern digital designs integrate hundreds of interconnected processing and memory units, the need for a higher level of abstraction to handle their descriptions is indisputable. This paper presents a beyond-RTL concurrent hardware description language that combines both Finite-State Machine (FSM) and constraint programming paradigms. At the featured level of abstraction, the user describes dynamic connections between data sources and sinks that may not always be ready to send or receive data tokens. The high-level description methodology enables a comprehensible description of behaviors such as data transfer synchronization, exclusivity, priority and constrained scheduling by the means of logical-implication rules constraining the data transfers authorizations. Dynamically connecting resources with potential combinatorial dependencies may lead to instability or deadlock. Such situations are automatically detected and fixed by the proposed compiler that generates a dedicated control-circuit optimizing the number of transfers that can be authorized at each clock cycle. The proposed design automation methodology is applied to the problem of deeply-pipelined vector reduction. A pipelined floating point accumulator and a matrix multiplication circuits are described with a few lines of code and automatically compiled into an FPGA. Results show that the synthesis results are comparable to those obtained with hand-written RTL but with much lower effort and time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".