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Record W7133099859

Selective Control-flow Reduction for Hardware Synthesis

2021· dissertation· W7133099859 on OpenAlexaff
Austin Liolli

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSoftwareControl flowBenchmark (surveying)High-level synthesisReduction (mathematics)Latency (audio)Flow control (data)Graph
DOInot available

Abstract

fetched live from OpenAlex

Control flow in a program can be represented in a directed graph, called the control flow graph (CFG). Nodes represent straight-line segments of code, basic blocks, and directed edges between nodes correspond to transfers of control. In this thesis, we present a methodology to selectively reduce control flow by collapsing basic blocks, providing increased instruction-level parallelism, and enabling the generation of pipelined circuits. Our work is motivated by the synthesis of hardware from software programs. Costing for potential collapses is determined by measuring the consequences on the speed performance of the generated hardware. We evaluate our framework within a high-level synthesis tool that allows a C-language software program to be automatically synthesized into a hardware circuit, using the CHStone benchmark suite, targeting an Intel Cyclone V FPGA. For individual benchmarks this yielded cycle latency reductions up to 20.7% and wall-clock time reductions up to 22.6%.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.328
Teacher spread0.304 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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