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Record W4233214088 · doi:10.32920/ryerson.14652543

Design and implementation of portable and configurable RISC processor architecture

2021· preprint· en· W4233214088 on OpenAlexaff
Volodymyr Sergeyev

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicroprocessorComputer scienceEmbedded systemPipeline (software)Reduced instruction set computingApplication-specific integrated circuitField-programmable gate arraySoftware portabilityComputer architectureComputer hardwareDesign flowARM architectureSoftwareInstruction setOperating system

Abstract

fetched live from OpenAlex

This project presents the configurable microprocessor design based on the MIPS architecture. The level of configurability includes a choice of the pipe lined or unpipelined architecture, number of pipeline stages, data path bit-width, instruction subsetting, program and data memory size. The microprocessor design flow is supported by the set of standard and custom software tools. The wide spectrum of the microprocessor configurations provides an opportunity to optimize hardware for the specific application. The HDL design of the microprocessor is independent of the hardware platform. The portability of the design was verified on the competitive FPGA platforms and ASIC. The selected microprocessor configuration running the test application was successfully implemented and verified on the FPGA development board. The obtained implementation results were compared to the existing commercial and research microprocessors and critical advantages of the presented design were outlined.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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