MétaCan
Menu
Back to cohort
Record W2153793450 · doi:10.1145/1508128.1508136

Towards reliable 5Gbps wave-pipelined and 3Gbps surfing interconnect in 65nm FPGAs

2009· article· en· W2153793450 on OpenAlexaff
Paul Teehan, Guy Lemieux, Mark R. Greenstreet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDatapathComputer scienceField-programmable gate arrayInterconnectionCrosstalkEmbedded systemBandwidth (computing)Noise (video)ThroughputParallel computingElectronic engineeringComputer networkWirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

FPGA user clocks are slow enough that only a fraction of the interconnect's bandwidth is actually used. There may be an opportunity to use throughput-oriented interconnect to decrease routing congestion and wire area using on-chip serial signaling, especially for datapath designs which operate on words instead of bits. To do so, these links must operate reliably at very high bit rates. We compare wave pipelining and surfing source-synchronous schemes in the presence of power supply and crosstalk noise. In particular, supply noise is a critical modeling challenge; better models are needed for FPGA power grids. Our results show that wave pipelining can operate at rates as high as 5Gbps for short links, but it is very sensitive to noise in longer links and must run much slower to be reliable. In contrast, surfing achieves a stable operating bit rate of 3Gbps and is relatively insensitive to noise.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicLow-power high-performance VLSI designFrench-language works237,207