An overview of technology, architecture and CAD tools for programmable logic devices
Bibliographic record
Abstract
Before the advent of programmable logic devices (PLDs), most digital hardware designs included significant numbers of small-scale integrated (SSI) circuits that comprised basic logic gates and flip-flops. However, modem designs contain virtually none of these low-density parts, but instead are built from more complex devices that consist of an uncommitted array of logic gates and memory elements that can be configured by the user to implement different circuits. The term programmable logic device is not easy to define because the assortment of chips that fall within this broad category, namely any integrated circuit that is programmable by the end user and intended for implementing hardware, has grown very large over the past few years. In fact, even the relevant terminology has become nebulous because of rapid technology changes and the introduction of new innovative architectures. The purpose of this paper is to provide an overview of programmable logic devices in order to present the reader with a clear view of what is available on the market today. The fundamental technologies employed to manufacture PLDs are presented, after which for each of the three categories of chips, namely simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field-programmable gate arrays (FPGAs), the paper describes the most significant architectural features, examples of applications, and the CAD tool design now typically used when implementing circuits.>
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".