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Record W2096081777 · doi:10.1109/fccm.2011.25

Towards Synthesis-Free JIT Compilation to Commodity FPGAs

2011· article· en· W2096081777 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
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceOverlayField-programmable gate arrayReconfigurabilityBenchmark (surveying)Embedded systemCompilerCode (set theory)Parallel computingKey (lock)Computer architectureSet (abstract data type)Operating systemProgramming language

Abstract

fetched live from OpenAlex

We explore the feasibility of accelerating soft processors by dynamically translating hot segments of code into FPGA circuits. We propose an approach that tackles two key challenges: the prohibitive compile time of standard synthesis tools and the limited run-time reconfigurability of commodity FPGAs. We use traces, or hot straight-line segments of code, as the units of code to translate into FPGA circuits, combined with a pre-synthesized overlay that is tuned for traces. The overlay, referred to as the Virtual Dynamically Reconfigurable (VDR) overlay consists of an array of functional units that are interconnected by a set of programmable switches. The overlay can be rapidly configured by the soft processor at run-time. Our approach avoids traditional synthesis and reduces code-to-circuit translation to the significantly faster mapping of instructions to VDR units. Preliminary evaluation shows that the overlay speeds up the execution of the benchmark by up to 9X over a Nios II processor. The overlay incurs a 6.4X penalty in resources compared to Nios II.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.001
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 head0.079
GPT teacher head0.266
Teacher spread0.187 · 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

Citations25
Published2011
Admission routes1
Has abstractyes

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