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Record W2083469609 · doi:10.5555/2133429.2133470

Credit borrow and repay: sharing DRAM with minimum latency and bandwidth guarantees

2010· article· en· W2083469609 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

VenueInternational Conference on Computer Aided Design · 2010
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceDramLatency (audio)Computer networkCacheQuality of serviceScheduling (production processes)Bandwidth (computing)CAS latencyDistributed computingOperating systemComputer hardwareMemory controllerTelecommunications

Abstract

fetched live from OpenAlex

Multi-port memory controllers (MPMC) play an important role in system-on-chips by coordinating accesses from different subsystems to shared DRAMs. The main challenge of MPMC design is optimize quality-of-service by simultaneously satisfying different---and often competing---requirements, including bandwidth and latency. While previous works have attempted to address the challenge, the proposed solutions are heuristic and often cannot provide bandwidth and/or latency guarantees. In this paper, we propose a new technique called Credit-Borrow-and-Repay (CBR) that augments a dynamic scheduling algorithm drawn from the networking community, improving it to achieve minimum latency while preserving minimum bandwidth guarantees. Our experiments show that on typical multimedia workloads, the cache response latency can be improved as much as 2.5X.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.804

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 communication0.0010.001
Open science0.0010.000
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 head0.032
GPT teacher head0.270
Teacher spread0.237 · 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