High Level Synthesis for Data-Driven Applications
Bibliographic record
Abstract
John von Neumann proposed his famous architecture in a context where hardware was very expensive and bulky. His goal was to maximize functionality with minimal hardware. Presently, logical gates are nearly free and single chips contain billions of gates. However, most current designs are still based on Von Neumann's architecture because processors are built on this model. Nevertheless, the main current challenge is to be able to design, refine, synthesize and verify new architectures in a minimum time and with a maximum computational performance regardless of the gate count. Data driven architectures enable a high level of parallelism because instead of a single controller managing all the resources (and often a single ALU), tens or hundreds of small controllers can now operate in parallel on local processing units. This paper presents an environment for the high level description, refinement, synthesis and verification of such systems. Our own HDL is presented with its compiler and we show how it can be used as the intermediate language of a compiler for an even higher level functional programming language. Ongoing work enables the interfacing with other languages (from both hardware and software communities). We also intend to target asynchronous designs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".