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Record W1965206671 · doi:10.5555/1015090.1015327

Automatic process migration of datapath hard IP libraries

2004· article· en· W1965206671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsia and South Pacific Design Automation Conference · 2004
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDatapathComputer architectureComputer scienceProcess (computing)ReusabilityIntegrated circuit designEmbedded systemSoftwareOperating system

Abstract

fetched live from OpenAlex

While essential for high-performance circuit design, the custom nature of datapath components confines their use in only a few microprocessor companies. The reusability of datapath intellectual property (IP) libraries is largely limited by their dependence on process technology. Layout migration tools today, which are based on layout compaction developed decades ago, cannot cope with the challenges involved. In this paper, we present a comprehensive datapath IP development framework that can perform process migration by accommodating advanced circuit considerations, layout architecture and transistor sizing, in addition to design rule satisfaction. We demonstrate the effectiveness of the framework by migrating the Berkeley low power library, originally developed for 1.2um MOSIS process, into TSMC 0.25um and 0.18um technology.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.582

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 communication0.0000.001
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 head0.045
GPT teacher head0.239
Teacher spread0.194 · 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