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Record W1977549083 · doi:10.1109/cjece.2013.6704692

Redesigned CMOS (4; 2) compressor for fast binary multipliers

2013· article· en· W1977549083 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsnot available
Fundersnot available
KeywordsXNOR gateGas compressorOperandComputer scienceRedundancy (engineering)Binary numberCMOSComputer hardwareAdderNAND gateArithmeticLogic gateParallel computingElectronic engineeringAlgorithmMathematicsEngineering

Abstract

fetched live from OpenAlex

(4; 2) compressors seem to be the most popular bit-compressing cells with principal application in multi-operand addition and multiplication hardware. Therefore, performance of (4; 2) compressors is particularly influential in the efficiency of multiplication intensive computations. Realization of these cells is mainly based on XOR/XNOR gates, which are functionally equivalent to three simpler ones among AND/NAND and OR/NOR gates. Decomposition of XOR/XNOR gates in some (4; 2) compressors to their constituent simpler ones may lead to removal of some hardware redundancy. In this paper we take advantage of such decomposition to propose a new (4; 2) compressor design, evaluate its performance, and compare it with previous designs. The proposed (4; 2) compressor, as such, and those of reference works are simulated with HSPICE using 45nm post-layout CCMOS standard cell library with presence of process variation. The results show performance improvements, compared to the best of reference designs, in terms of delay (17%), power (13%), and power-delay-product (30%). For more realistic comparison, performance of each design is evaluated via incorporation of more than 1300 (4; 2) compressors in 54×54-bit binary multipliers as a uniform test bench via MAGMA tools. This experience confirmed the above results on isolated single (4; 2) compressors.

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: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score0.730

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)0.0000.000

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 head0.004
GPT teacher head0.150
Teacher spread0.146 · 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