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Record W1996426321 · doi:10.1145/2000832.2000840

Leveraging reconfigurability in the hardware/software codesign process

2011· article· en· W1996426321 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

VenueACM Transactions on Reconfigurable Technology and Systems · 2011
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsReconfigurabilityComputer scienceEmbedded systemField-programmable gate arrayComputer architectureSystem on a chipFPGA prototypeFlexibility (engineering)SoftwareElectronic system-level design and verificationProcess (computing)Systems designReconfigurable computingProfiling (computer programming)Design processOperating systemSoftware engineeringWork in process

Abstract

fetched live from OpenAlex

Current technology allows designers to implement complete embedded computing systems on a single FPGA. Using an FPGA as the implementation platform introduces greater flexibility into the design process and allows a new approach to embedded system design. Since there is no cost to reprogramming an FPGA, system performance can be measured on-chip in the runtime environment and the system's architecture can be altered based on an evaluation of the data to meet design requirements. In this article, we discuss a new hardware/software codesign methodology tailored to reconfigurable platforms and a design infrastructure created to incorporate on-chip design tools. This methodology utilizes the FPGA's reconfigurability during the design process to profile and verify system performance, thereby reducing system design time. Our current design infrastructure includes: a system specification tool, two on-chip profiling tools, and an on-chip system verification tool.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.264
Teacher spread0.205 · 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