An architecture exploration framework for the implementation of embedded dsp applications
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. Using SoC technology increases the challenges facing a designer when choosing an appropriate design. A tool that helps explore different architectures is required to design such an efficient system. The tool should be able to explore different architectures and evaluate them according to the given constraints. The design space to be explored depends on the application domain and the target platform. This thesis proposes an efficient Particle Swarm Optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength-Pareto approach originally used in Evolutionary Algorithms (EA). The proposed modified particle-swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO) and the proposed strength Pareto PSO based on different metrics. Accordingly, an optimization engine is built using these meta-heuristics that can be used to solve multi-objective optimization problems in general and design exploration in particular. The direction of the search process depends on the evaluation of each solution generated. In this thesis an approach for performance evaluation of embedded systems is presented. Several cycle-accurate simulations are performed for commercial embedded processors used in our study. The simulation results are used to build Artificial Neural Network (ANN) models with accuracy up to 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to increase the accuracy of the estimation process. The optimization engine is integrated with the performance evaluation module to build an architecture exploration framework for embedded DSP applications. The functionality of the framework is verified by using benchmarks from industry. The results show accuracy over 90% comparing the proposed solutions to cycle-accurate simulation.
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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 | 0.001 | 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 | 0.000 |
| 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 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".