Hardware Acceleration of Online Error Detection in Many-Core Processors
Bibliographic record
Abstract
Due to worsening aging effects and incomplete testing and verification processes, systems being built in new fabrication technologies have encountered serious reliability challenges. A promising solution to these reliability challenges is self-test and self-reconfiguration with no or limited external control. In this paper, a scalable self-test mechanism for online testing of many-core processors has been proposed. Several hardware components are incorporated in the many-core architecture that distribute software test routines among the processing cores, monitor behavior of the processing cores during test routine execution, and detect faulty cores. A merit-based probabilistic test generation (MPTG) method to generate test for register-transfer level components considering the limitations imposed by the neighboring components has been proposed. In addition, a test generation approach utilizing MPTG has been proposed for software test routine generation in this environment. Experimental results show that the proposed test generation method results in good stuck-at fault coverage in a limited number of test cycles. In addition, the proposed test mechanism is extensively scalable in terms of hardware and timing overhead making it applicable to many-cores with a large number of processing cores.
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 | 0.000 |
| 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".