Rapid And Efficient Multi Objective Design Space Exploration Methods In High Level Synthesis Of Computation Intensive Applications
Bibliographic record
Abstract
Design Space Exploration (DSE) is an indispensable segment of the High Level Synthesis (HLS) design process. Moreover, the enormous increase in complexity of the recent Very Large Scale Integration (VLSI) circuits has only been possible due to use of advan ced DSE techniquesduring HLS process. This dissertation presents four automated optimization algorithms and methodologies that are capable to handle various multi-objective problems during design space exploration and high level synthesis of computation intensive applications. Algorithmic solutions to four different branches of DSE problems have been proposed in this dissertation viz. a) Solution to power-performance-area/cost trade-off of Digital Signal Processing (DSP) kernels using priority factor process which also includes deriving analytical mathematical model for modern performance parametric frameworks b) Solution to area-performance-power tradeoff/ power-performance-area tradeoff of DSP kernels using hybridization of fuzzy algorithm and vector design space technique with Self-Correction Scheme c) Solution to dual parametric optimization using efficient multi structure genetic algorithm for integrated scheduling and allocation and d) Solution to control step bound static power optimization using power gradient methodology for integrated scheduling and allocation. Some techniques proposed are equipped with pipelined execution time parameter (based on need), in addition to hardware area, power and cost depending on the user’s objective for exploration of a final solution in a short time. In addition to architecture exploration capability, rapid automated circuit generation of DSP kernels is also possible in a short time for verification and synthesis in Field Programmable Gate Array (FPGA) platforms. The proposed exploration approaches are applied to custom data intensive applications application specific processors/custom processors) or standalone Application Specific Integrated Circuits (ASIC’s). Results of the experiments for proposed approaches on all the standard DSP benchmarks have indicated improvements either in terms of exploration runtime, quality of final solution, reduced execution time, power and area or a multiple combination of all factors when compared to recent approaches.
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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 | 0.001 |
| 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".