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Record W4388214731 · doi:10.1109/fpl60245.2023.00016

Titan 2.0: Enabling Open-Source CAD Evaluation with a Modern Architecture Capture

2023· article· en· W4388214731 on OpenAlexafffund
Vaughn Betz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaIntel Corporation
KeywordsStratixTitan (rocket family)Computer scienceField-programmable gate arrayArchitectureComputer architectureEmbedded systemBenchmarkingParallel computingMemory footprintOperating systemEngineering

Abstract

fetched live from OpenAlex

This paper presents an updated version of the Titan (Quartus + VPR) flow, and a VPR-compatible architecture description of Intel's Stratix 10 device. Together these components enable large designs to be targeted at a complex and realistic architecture, facilitating improved benchmarking and optimization of open-source CAD flows. Additionally, the Stratix 10 architecture capture is a useful baseline architecture on which proposed new FPGA features can be evaluated. We capture the device primitives, intra-block connectivity, device floorplans, routing architecture and timing with reasonable fidelity in the human-readable VTR architecture format, enabling easy modification by researchers. Using the updated Titan flow and Stratix 10 capture, we compare the quality of results (QoR), runtime, and resource utilization of VPR to Quartus Prime. The results show that VPR's overall runtime is comparable and its memory footprint is smaller than that of Quartus, but the packer runtime is higher and Quartus achieves better wirelength and frequency. We identify causes of the slower packing runtime in VPR and suggest directions for future improvements.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.257
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes2
Has abstractyes

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