Exploration of optimal multi-cycle transient fault secured datapath during high level synthesis based on user area-delay budget
Bibliographic record
Abstract
Detecting error or producing correct output is the primary function of a fault secured system. In the context of multi-cycle transient faults, design space exploration (DSE) of an optimal fault secured datapath based on user constraints of area and delay during high level synthesis (HLS) is considered notorious. This is derived from the fact that generation of a user budget bounded multi-cycle transient fault secured datapath may not be possible for every type of candidate design solution produced during exploration. Additionally, insertion of inapt cut to optimize delay overhead associated with fault security in most cases may not yield optimal solutions in the context of user constraints/budgets. This paper resolves the above problems which has not been addressed in the literature so far by proposing the following novelties: (a) fault secured particle swarm optimization (PSO) driven DSE methodology (b) Techniques to handle multi-cycle transient faults during DSE (c) Schemes for choosing pertinent edges for inserting cut (s) in scheduled Control Data Flow Graph (CDFG) that optimizes the delay overhead associated with fault security. Results of the proposed approach indicated that the fault secured solution found comprehensively minimizes the final cost as well as satisfies the conflicting user budgets. Further, the final fault secured solution yielded is significantly lower in cost compared to solutions obtained through recent similar approaches.
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".