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Record W2043057288 · doi:10.1109/fpl.2013.6645536

Low-cost, high-performance branch predictors for soft processors

2013· article· en· W2043057288 on OpenAlex

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
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStratixBranch predictorField-programmable gate arrayComputer scienceParallel computingDecoding methodsImplementationStack (abstract data type)Embedded systemComputer hardwareComputer architectureAlgorithmOperating system

Abstract

fetched live from OpenAlex

This work studies branch predictor implementations for general purpose, pipelined, single core soft processors. It shows that the existing designs do not map well onto reconfigurable hardware since they were optimized for custom logic implementation. This work then proposes an accurate and fast branch predictor that uses few resources on FPGAs. The proposed predictor uses: (1) an FPGA-friendly pattern based direction predictor, (2) a return address stack, (3) in-fetch target address calculation instead of a branch target buffer, and (4) instruction pre-decoding. Experimental measurements using a subset of the SPECCPU2006 workloads show that the presented FPGA-friendly branch predictor delivers high performance while operating at approximately 259 MHz using only 147 ALUTs and one BRAM on an Altera Stratix IV 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.436

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 communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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