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Record W4206631564 · doi:10.1109/tc.2021.3133828

Stop and Look: A Novel Checkpointing and Debugging Flow for FPGAs

2021· article· en· W4206631564 on OpenAlex
Sameh Attia, Vaughn Betz

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 Computers · 2021
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceDebuggingEmbedded systemState (computer science)Overhead (engineering)FPGA prototypePlace and routeFinite-state machineContext (archaeology)Reconfigurable computingComputer hardwareOperating system

Abstract

fetched live from OpenAlex

Hardware checkpointing enables live migration, fault recovery, and context switching, but has been difficult to achieve for FPGA applications. We detail techniques to checkpoint complex FPGA designs and develop StateMover, a new checkpoint-based debugging flow for FPGAs that combines the speed of hardware execution with the full observability and controllability of simulation. StateMover can safely stop a running design and seamlessly move its state back and forth between an FPGA and a simulator. StateMover can create complete design checkpoints even for designs that have multi-cycle I/O interfaces, contain buried state that is not accessible by FPGA readback, and use external memories. StateMover and its associated IPs allow a designer to quickly make a design checkpointable, with a small area overhead. Moving the state from/to an FPGA to/from a simulator can be performed in a few seconds for large Xilinx UltraScale FPGAs.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.576

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 communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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