Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static Scheduling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high computation power and tight constraints. The design space to be explored depends on the application domain and the target platform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an architecture exploration framework for DSP applications based on Particle Swarm Optimization (PSO) and genetic algorithms (GA) techniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance evaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded processors. These simulation results are used to build an artificial neural network (ANN) model that can predict performance/power of newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to further increase the accuracy of the estimation process. The functionality of the framework is verified based on benchmarks provided by our industrial partner ON Semiconductor to illustrate the ability of the framework to investigate the design space.
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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 | 0.001 | 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 | 0.000 | 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 it