MétaCan
Menu
Back to cohort
Record W2289241984 · doi:10.1109/rsp.2015.7416557

Hard block reduction and synthesis improvements in Odin II

2015· article· en· W2289241984 on OpenAlexafffund
Bo Yan, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsNetlistComputer scienceVerilogField-programmable gate arrayBlock (permutation group theory)Reduction (mathematics)Computer architectureFor loopEmbedded systemRouting (electronic design automation)Abstract syntax treeTree (set theory)Gate arrayProgramming languageComputer hardwareLoop (graph theory)Parsing

Abstract

fetched live from OpenAlex

A Field-Programmable Gate Array (FPGA) is an integrated circuit that allows users to program product features and functions after manufacturing. Verilog-to-Routing (VTR) is an open source CAD tool for conducting FPGA architecture and CAD research and development. As one of the core tools of VTR, Odin II is responsible for Verilog elaboration and hard block synthesis. This project describes the improvements in Odin II on three aspects: for loop support, abstract syntax tree (AST) simplification and hard block reduction. This work allows elaboration of a for loop statement by modifying the Abstract Syntax Tree (AST). There are different alternatives to simplify an AST, and this paper demonstrates three ways: simplifying expressions with variables, reducing parameters with values and using shift operations to replace multiplications or divisions. For a circuit design, some hard blocks in the netlist have the same high-level function. This project further provides a method to reduce redundant hard blocks. Each implementation is tested with designed testing cases or sets of benchmarks, and the results of running them through Odin II and VTR are demonstrated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.214

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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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 designBench or experimental
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

Citations2
Published2015
Admission routes2
Has abstractyes

Explore more

Same topicVLSI and FPGA Design TechniquesFrench-language works237,207