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Record W1969546595 · doi:10.1109/tcad.2013.2269768

An Instruction Scratchpad Memory Allocation for the Precision Timed Architecture

2013· article· en· W1969546595 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

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2013
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceBenchmark (surveying)Register allocationArchitectureScheme (mathematics)Worst-case execution timeParallel computingGraphInteger programmingControl flowControl flow graphExecution timeTheoretical computer scienceProgramming languageCompilerAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a static instruction scratchpad memory allocation scheme for the precision timed architecture (PRET). Since PRET provides timing instructions to control the temporal execution of programs, the objective of the allocation scheme is to ensure that the explicitly specified temporal requirements are met. Furthermore, this allocation incorporates the timing requirements from the multiple hardware threads of the PRET architecture. We formulate the allocation problem as an integer-linear programming problem, and we implement a tool that takes compiled ARMv4 binaries, constructs a timing-requirements-aware control-flow graph, performs a WCET analysis and SPM allocation, and rewrites the binaries with the allocation. We evaluate our approach using a modified version of the Malardalen benchmarks to show the benefits of the proposed approach. We also present a UAV benchmark derived from the PapaBench benchmark.

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 categoriesMeta-epidemiology (narrow)
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 score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.028
GPT teacher head0.248
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