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Record W1669114967 · doi:10.1109/fpl.2015.7293955

Synthesizable FPGA fabrics targetable by the Verilog-to-Routing (VTR) CAD flow

2015· article· en· W1669114967 on OpenAlex
Jin Hee Kim, Jason H. Anderson

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayVerilogComputer scienceFPGA prototypeBitstreamEmbedded systemRouting (electronic design automation)Application-specific integrated circuitComputer architectureComputer hardware

Abstract

fetched live from OpenAlex

We consider implementing FPGAs using a standard cell design methodology, and present a framework for the automated generation of synthesizable FPGA fabrics. The open-source Verilog-to-Routing (VTR) FPGA architecture evaluation framework [1] is extended to generate synthesizable Verilog for its in-memory FPGA architectural device model. The Verilog can be synthesized into standard cells, placed and routed using an ASIC design flow. A second extension to VTR generates a configuration bitstream for the FPGA; that is, the bitstream configures the FPGA to realize a user-provided placed and routed design. The proposed framework and methodology opens the door to silicon implementation of a wide range of VTR-modelled FPGA fabrics. In an experimental study, area and timing-optimized FPGA implementations in 65nm TSMC standard cells are compared with a 65nm Altera commercial FPGA.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.527

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 head0.018
GPT teacher head0.208
Teacher spread0.190 · 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

Quick stats

Citations21
Published2015
Admission routes1
Has abstractyes

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