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A Common Recursive Form for Multiple Fundamental Arithmetic Operators and its Automated Synthesis

2019· article· en· W3013013881 on OpenAlexaff
Sébastien Roy, Frédéric Mailhot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceArithmeticVerilogAdderVHDLLogic synthesisArithmetic logic unitLogic gateField-programmable gate arrayParallel computingAlgorithmMathematicsComputer hardware

Abstract

fetched live from OpenAlex

The common recursive structure of a number of basic arithmetic operators is investigated. Previously, it was shown that fully combinational leading-digit N-bit detector circuits could be generated with a simple dyadic tree recursive structure having minimal complexity, regular and low fan-in and fan-out and log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (N) stages of delay for all outputs. Here, we show that this recursive structure is shared by many common arithmetic functions, including comparison, incrementation, decrementation, and fast parallel-prefix adder. This enables said functions to be described simply and parametrically in VHDL or Verilog using structural recursion. The commonality can also be leveraged to design multi-function circuits which are advantageous compared to separate operators chosen through multiplexers in, e.g., arithmetic-logic units. Preliminary synthesis results for both FPGA and digital CMOS are provided in order to characterize said benefits. This type of multifunction circuit could be employed in the design of fast, low-complexity arithmetic-logic units (ALUs) inside microprocessors, digital signal processors, or application-specific system-on-chip (SoC) designs.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
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 head0.008
GPT teacher head0.214
Teacher spread0.205 · 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.

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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