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Record W4281719745 · doi:10.1145/3526241.3530336

A Novel 2T2R CR-based TCAM Design for High-speed and Energy-efficient Applications

2022· article· en· W4281719745 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

VenueProceedings of the Great Lakes Symposium on VLSI 2022 · 2022
Typearticle
Languageen
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer architectureEnergy (signal processing)Embedded systemPhysics

Abstract

fetched live from OpenAlex

A 2T2R current race (CR) based ternary content addressable memory (TCAM) design is proposed using resistive random-access memory (RRAM) technology. The suggested design adopts a match-line (ML) booster feature in sensing amplifier to improve search speed and tolerance to RRAM switching variations. An SR-latch cascading scheme is presented to further improve the speed and energy efficiency for large TCAM array. Additionally, a same clock phase cascading scheme is proposed to reduce latency in cascading structure, by placing evaluation phase of all stages in the same clock phase. With the suggested ML booster, our 64-bit 1-stage design has speed and energy consumption matching the best performance reported by other emerging non-volatile memory (eNVM) based TCAM design. Our 128-bit 2-stage design also has comparable speed and energy to SRAM-based TCAM design with significantly more compact size (90% reduction) and non-volatility.

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 score0.934
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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