Power Efficient Rapid Design Space Exploration of Integrated Scheduling and Module Selection in High Level Synthesis
Bibliographic record
Abstract
High level Synthesis (HLS) or Electronic System Level (ESL) synthesis requires scheduling algorithms that have strong capability to reach optimal/near-optimal solutions with significant rapidity and greater accuracy. A novel power efficient scheduling approach using ‘PI’ method has been presented in this thesis that reduces the final power consumption of the solution at the expenditure of minimal latency clock cycles. The proposed scheduling approach is based on ‘Priority indicator (PI)’ metric and ‘Intersect Matrix’ topology methods that have a tendency to escape local optimal solutions and thereby reach global solutions. Application of the proposed approach results in even distribution of allocated hardware functional units thereby yielding power efficient scheduling solutions. The two main novel and significant aspects of the thesis are: a) Introduction of ‘Intersect Matrix’ topology with its associated algorithm which is used to check for precedence violation during scheduling b) Introduction of PI method using Priority indicator metric that assists in choosing the highest priority node during each iteration of the scheduling optimization process. Comparative analysis of the proposed approach has been done with an existing design space exploration method for qualitative assessment using proposed ‘Quality Cost Factor (Q- metric)’. This Q-metric is a combination of latency and power consumption values for the solution found, which dictates the quality of the final solutions found in terms of cost for both the proposed and existing approaches. An average improvement of approximately 12 % in quality of final solution and average reduction of 59 % in runtime has been achieved by the proposed approach compared to a current scheduling approach for the DSP benchmarks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".