</title> </titles> <publication_date media_type='print'> <month>8</month> <year>2013</year> </publication_date> <publication_date media_type='online'> <month>8</month> <year>2013</year> </publication_date> <doi_data> <doi></doi> <resource></resource> </doi_data> </journal_article> <!-- ============== --> <journal_article publication_type='full_text'> <titles> <title>Performance Analysis of a Reconfigurable Shared Memory Multiprocessor System for Embedded Applications
Bibliographic record
Abstract
This paper presents a method to predict perform processor cores in a reconfigurable system for embedded applications. A multiprocessor framework is developed with the capability of reconfigurable processors in a shared memory system optimized for stream signal processing applications. The framework features a discrete time Markov based stochastic tool, which is used to analyze memory contention in the shared memory architecture, and to predict the performance increase (speed of execution) when the number of processors i variations of other system parameters, such as different task allocations and the number of pipeline stages are possible were verified by experimental results of a green scre and run on a Xilinx Virtex
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 | 0.030 | 0.011 |
| Meta-epidemiology (narrow) | 0.011 | 0.011 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.018 | 0.030 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.033 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.022 |
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; both teacher heads agree on what is shown here.
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".