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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Integration of multiple coinflip devices for high-quality random sampling.

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Generating high-quality random numbers is crucial for artificial intelligence and scientific computing. This study uses tunnel diodes as coinflip devices, developing a system to produce reliable random bitstreams for probabilistic computing applications.

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Area of Science:

  • Stochastic computing
  • Microelectronic device engineering
  • Applied physics

Background:

  • Artificial intelligence, scientific computing, and probabilistic computing rely on random sampling, necessitating vast quantities of random numbers.
  • Stochastic microelectronic devices, or coinflip devices, offer a potential solution for high-rate random bit generation but suffer from analog nonidealities like temperature dependence and drift.
  • These nonidealities can introduce determinism, compromising the quality of the generated random bitstreams.

Purpose of the Study:

  • To address the challenges of nonidealities in coinflip devices for generating high-quality random bitstreams.
  • To develop a system for producing reliable and unpredictable random bits suitable for probabilistic computing.
  • To demonstrate the practical application of these bitstreams in a Monte Carlo approximation.

Main Methods:

  • Exploration of coinflip devices, specifically tunnel diodes, for random bit generation.
  • Implementation of a control loop to mitigate temperature dependence and ensure fair bit generation from individual devices.
  • Parallel combination of bitstreams from multiple tunnel diodes to achieve fair and unpredictable outputs.

Main Results:

  • A control loop was successfully implemented to adapt to temperature variations in tunnel diode coinflip devices, yielding fair bitstreams.
  • Combining parallel bitstreams from individual tunnel diodes produced high-quality, fair, and unpredictable random bitstreams.
  • The generated bitstreams were validated for their suitability in probabilistic computing through a Monte Carlo approximation of pi.

Conclusions:

  • Systems of multiple coinflip devices, when properly managed, can overcome analog nonidealities to produce high-quality random bitstreams.
  • The developed method provides a viable approach for generating the large volumes of random numbers required by advanced computing paradigms.
  • The successful Monte Carlo approximation demonstrates the practical utility of these engineered random bitstreams.