High Level Synthesis for Data-Driven Applications
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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