DP-BIST: a built-in self-test for DSP data paths-a low overhead and high fault coverage technique
Bibliographic record
Abstract
A new Built-In Self Test (BIST) technique suitable for high performance DSP datapaths is presented. The BIST session is controlled via hardware without the need for a separate test pattern generation register or test program storage. Furthermore, the BIST scenario is appropriately set-up so as to also test the register file as well as the shift and truncation logic in the datapath. The use of DP-BIST enables a very high speed test (one test vector is applied per clock cycle) with no performance degradation and little area overhead for the hardware test control. Comparison between DP-BIST and scan based BIST technique is also presented. We show how DB-BIST can be used a centralized test resource to test other macros on the chip and the integration of DP-BIST with internal scan and boundary scan is addressed.
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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 | 0.001 | 0.001 |
| 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".