Development Platform for Floating-Point Analog-to-Digital Converters
Bibliographic record
Abstract
The floating-point analog-to-digital converter (FPADC) is an extended version of the fixed-point ADC being designed to deal with a broader dynamic range of signals while exhibiting a smaller relative quantization error. Since the FPADC is characterized by a high relative precision, it requires high-precision high-speed components. The cost of these high precision high-speed components limits the availability of FPADCs to high-priced designs. Several architectures of floating-point analog-to-digital converters (FPADC) were reported in the last years (Yang et al., 1999). Since FPADCs are usually tailored for specific applications, and since their design is a highly resource consuming process, there is a need for a flexible development platform. An emulator platform, described in this paper, been conceived and built for the emulation of this class of ADC's. This development environment allows for the tuning of the FP-ADC architectures and their parameters before starting the design process, such they best fit envisaged applications. This paper presents the complete architecture and the implementation of this hardware/software development environment for FP-ADCs
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".