An architecture exploration framework for DSP applications
Why this work is in the frame
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Bibliographic record
Abstract
Advances in chip technology have enabled integrating many functional units on a single chip. This led to the emergence of the concept of system-on-chip (SoC) which is used extensively in the development of advanced embedded systems. Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high computation power and tight constraints. Using SoC technology increases the challenges facing the designer to choose the optimal design. A tool that helps explore different architectures is required to design an efficient system. In this paper we propose an implementation of an architecture exploration frame-work based on a multi-objective evolutionary algorithm (MOEA) for DSP applications. The design space is based on an experimental core library, and an analytical evaluation approach is used to speedup fitness calculation. Results obtained indicate that the proposed approach is valid and efficient for solving the architecture exploration problem.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it