MétaCan
Menu
Back to cohort
Record W4307923852 · doi:10.32920/21463020

Introducing TAM: Time-Based Access Memory

2022· preprint· en· W4307923852 on OpenAlexaff
Nagi Mekhiel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceInterleaved memoryParallel computingUniform memory accessStatic random-access memoryAccess timeScalabilityRegistered memoryDramShared memoryCache-only memory architectureEmbedded systemMemory managementSemiconductor memoryComputer hardwareOperating system

Abstract

fetched live from OpenAlex

<p> </p> <p>The increase in processor speed achieved by continuous improvements in technology is causing major obstacles to the parallel processors implemented inside the chip. The time spent in servicing all the cache misses from all processors from a slow shared memory limits the performance gain of parallel processors. We propose a new memory system that makes all of its content available to processors, so that processors need not to access the shared memory in a serial fashion. Rather than having one processor access a single location in the shared memory at a time, we force each location to be available to all processors at a specific time. This new memory system is fast and simple, because it does not need decoders and can use the DRAM or SRAM technology efficiently as the access of each location is known ahead of time. Results show that this new memory improves a single processor performance by 350% and the performance of eight parallel processors by 2400%. The new memory decouples the slow memory from the fast processor and makes the parallel processors scalable to an infinite number of processors.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.049
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0050.008
Research integrity0.0000.001
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 head0.024
GPT teacher head0.296
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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207