A hardware test platform in field-programmable logic for parallelized digital signal processing
Bibliographic record
Abstract
This paper describes the development and application of a self-contained platform to support hardware implementation and testing of components and systems for parallelized digital signal processing in field-programmable gate-array (FPGA) logic chips. To more closely reflect the usage of parallelized components in actual applications, high-speed input/output pins of FPGA chips are utilized in a loopback configuration so that parallel streams of representative digitized complex fixed-point data are fed as input to a system under test. Onchip FPGA memory is used to implement high-speed FIFO buffers connected to the system under test. On-chip memory is also used to stage data for the input FIFO buffer feeding the system under test and to collect processed data from the output FIFO buffer. An embedded processor executes software used to perform data transfers between the FIFO buffers and staging memory, and to initiate full-speed operation for a system under test with data flowing through the external loopback connection. This paper also provides sample results from a representative parallelized finite-impulse-response filter tested using the hardware platform described in this paper.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".