Bibliographic record
Abstract
Programming abstractions decrease the cognitive gap between program idealization and expression. In the software domain, this high-level expressive power is achieved through layered abstractions - virtual machines, compilers, operating systems - which translate, at design and runtime, programmer visible code into hardware-compatible code. While this paradigm is ideal for static, i.e., unmodifiable, hardware, several of these abstractions break down when programming configurable hardware. State of the art hardware/software co-design techniques (e.g., High Level Synthesis (HLS), Intermediate Fabrics) are, for the most part, ad hoc patches to the traditional abstraction stack, applicable only to specific toolchains or software components. In this paper, we survey current hardware design and hardware/software co-design abstractions, from the perspective of the design language/toolchain. We perform a systematic analysis of different design paradigms, including HLS, Domain Specific Languages (DSL), and new-generation Hardware Description Languages (HDL). We analyze how these paradigms differ in expressiveness, support for hardware/software interaction, hierarchy and modularity, HDL interoperability, and interface with the outside world.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".