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Record W2140998818

Field-programmable gate array architectures and algorithms optimized for implementing datapath circuits

2004· article· en· W2140998818 on OpenAlexaff
Jonathan Rose, David Lewis, Andy Ye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDatapathField-programmable gate arrayApplication-specific integrated circuitComputer scienceElectronic circuitComputer architectureGate arrayComputer hardwareEmbedded systemEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Field-Programmable Gate Arrays (FPGAs) are user-programmable digital devices that provide efficient, yet flexible, implementations of digital circuits. Over the years, the logic capacity of FPGAs has been dramatically increased; and currently they are being used to implement large arithmetic-intensive applications, which contain a greater portion of datapath circuits. Each circuit, constructed out of multiple identical building blocks called bit-slices, has highly regular structures. These regular structures have been routinely exploited to increase speed and area-efficiency in designing custom Application Specific Integrated Circuits (ASIC). Previous research suggests that the implementation area of datapath circuits on FPGAs can also be significantly reduced by exploiting datapath regularity through an architectural feature called configuration memory sharing (CMS), which takes advantage of datapath regularity by sharing configuration memory bits across, normally independently controlled, reconfigurable FPGA resources. The results of these studies suggest that CMS can reduce the total area required to implement a datapath circuit on FPGA by as much as 50%. They, however, did not take into account detailed implementation issues such as transistor sizing, utilizable regularity in actual datapath circuits, and Computer-Aided Design (CAD) tool efficiencies. This study is the first major in-depth study on CMS. The study found that when detailed implementation issues are taken into account, the actual achievable area savings can be significant less than the previous estimations—the CMS architecture investigated in this study is only about 10% more area efficient than a comparable conventional and widely studied FPGA architecture for implementing datapath circuits. Furthermore, this increase in area efficiency has a potential speed penalty of around 10%. To conduct the study, a new area-efficient FPGA architecture is designed along with its supporting CAD tools. The architecture, called Multi-Bit FPGA (MB-FPGA), is the first completely specified FPGA architecture that employs CMS routing resources. This sharing significantly reduces the number of configuration memory bits and consequently increases its area efficiency. The use of the CMS resources, however, imposes new demands on the traditional FPGA CAD algorithms. As a result, a complete set of CAD tools supporting FPGAs containing CMS resources are proposed and implemented. These tools are designed to extract and utilize datapath regularity for the CMS resources. It is shown that these tools yield excellent results for implementing a set of realistic datapath circuits on the MB-FPGA architecture.

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: Methods · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.773

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 head0.015
GPT teacher head0.238
Teacher spread0.224 · 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
GenreMethods

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

Citations12
Published2004
Admission routes1
Has abstractyes

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