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Record W4285346953 · doi:10.1109/rsp53691.2021.9806205

Heterogeneous Logic Implementation for Adders in VTR

2021· article· en· W4285346953 on OpenAlexaff
Harpreet Kaur, Georgiy Krylov, Seyed Alireza Damghani, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAdderVerilogComputer scienceField-programmable gate arrayLogic blockRouting (electronic design automation)Block (permutation group theory)Computer architectureLogic synthesisScheme (mathematics)Parallel computingLogic gateCritical path methodDesign flowComputer hardwareEmbedded systemEngineeringAlgorithm

Abstract

fetched live from OpenAlex

Verilog-to-Routing (VTR) is a Field-Programmable Gate Array (FPGA) Computer-Aided Design (CAD) tool. It is composed of three tools, namely ODIN II, ABC and VPR with each performing distinctive optimizations at different stages of the design flow. The elaboration and hard block synthesis stage of VTR is the core responsibility of the sub-project ODIN II. This work enables ODIN II to use fewer hard adders in the circuit by allowing soft logic implementation alongside hard logic for circuits featuring addition operations. This is particularly useful in scenarios where a sufficient number of hard blocks are not available. The results of applying our modifications to ODIN II as well as the entire VTR flow have been analysed. The results reveal the potential of current adder optimizations to achieve up to 17% performance gains in terms of critical path delays. Another effect of the optimization is the implications on the resulting device size. Some future prospects in this respect are also outlined in this paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.867
Threshold uncertainty score0.296

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 head0.288
Teacher spread0.267 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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