Bibliographic record
Abstract
Modern FPGAs comprise ever more complex blocks to enable a wide variety of customer applications. Verification of the complex blocks can be a time consuming process, especially at the late stages of the release cycle. A key challenge is the time it takes to create circuits that can run on a target device to test a given block. This paper demonstrates how High-Level Design tools, such as Altera SDK for OpenCL, can be utilized to aid in this work to verify the operation of complex hardened blocks. As a proof of concept, we present the methodology used to verify the correctness of hardened single-precision floating point adder, subtractor and multiplier units on Altera Arria 10 FPGA in a single day. Each design comprised an instance of a hardened floating point unit, either an adder, subtractor or a multiplier, and a functional equivalent there of implemented purely using Lookup Tables (LUTs). Both the hardened module instance and the LUT implementation were generated from OpenCL description using Altera SDK for OpenCL. The results for each computation were compared between the two implementations and any single discrepancy constituted a test failure. To simplify the test, the I/O for each design comprised LEDs (for pass/fail/running/done status) and two switches -- start and reset.
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 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".