MétaCan
Menu
Back to cohort
Record W4230411144 · doi:10.32920/ryerson.14657358.v1

Rapid And Efficient Multi Objective Design Space Exploration Methods In High Level Synthesis Of Computation Intensive Applications

2021· preprint· en· W4230411144 on OpenAlexaff
Anirban Sengupta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHigh-level synthesisDesign space explorationComputer scienceField-programmable gate arrayDigital signal processingScheduling (production processes)Very-large-scale integrationParametric statisticsComputer engineeringEmbedded systemMathematical optimizationComputer hardwareMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.569
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.144
GPT teacher head0.370
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicEmbedded Systems Design TechniquesFrench-language works237,207