MétaCan
Menu
Back to cohort

RISC-V Based Processor Architecture for an Embedded Visible Light Spectrophotometer

2022· article· en· W4308091040 on OpenAlexafffund
Guillaume Soulard, Gabriel Lachance, Élodie Boisselier, Mounir Boukadoum, Amine Miled

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversité Laval
FundersCMC Microsystems
KeywordsComputer scienceReduced instruction set computingField-programmable gate arrayMiniaturizationEmbedded systemComputer hardwareCyclone (programming language)Interface (matter)System on a chipComputer architectureInstruction setElectrical engineeringEngineeringParallel computing

Abstract

fetched live from OpenAlex

The miniaturization of sensing systems often requires embedding an electronic subsystem for local or edge computing, or to interface with the sensor for pre-processing operations. The sensing part of the work presented in this paper is an optoelectronic system that measures neurotransmitters concentration based on visible spectroscopy and that is currently implemented with an external processor in a computer. This paper presents a System on a Chip (SoC) design based on the RISC-V processor and the required peripheral interfaces to replace the current system. The new design is first simulated and then implemented on Cyclone IV and Xilinx ZCU102 FPGAs to explore the usability and advantages of the approach. Both architectures were similar in terms of memory and register use, but the ZCU102-based system used 18016 logic elements, while the Cyclone IV-based one used much less, 13468 logic elements. We also observed a significant difference in frequency of operation, with Cyclone IV was running at 27.84 MHz and ZCU102 at 125 MHz clock speeds.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

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)0.0010.000

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 head0.225
Teacher spread0.217 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicCCD and CMOS Imaging SensorsFrench-language works237,207