Generating Stochastic Bitstreams

Hsiao, Hsuan; Anderson, Jason; Hara-Azumi, Yuko

doi:10.1007/978-3-030-03730-7_7

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Such effects can be utilized not only for AmoebaSAT but also for other algorithms that require fluctuations, such as stochastic computing. 26)…”

Section: Results Of Ev2mentioning

confidence: 99%

“…14,15) Those hardware-oriented algorithms realized fluctuations by pseudo random number generators (PRNGs) with the currently-available digital devices in mind such as FPGA. As have been discussed in other works using random numbers, 26) PRNGs largely affect not only the solution search performance but also the circuit area. Hence, "how many fluctuations would be needed?"…”

Section: Motivationmentioning

confidence: 86%

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Digital bio-inspired satisfiability solver leveraging fluctuations

Hara-Azumi

Takeuchi

Hara

et al. 2020

Jpn. J. Appl. Phys.

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As the end of Dennard scaling is almost approaching, a number of bio-inspired algorithms have been gaining attention these days to realize new types of computing models and architectures. AmoebaSAT is one of such algorithms that can efficiently solve satisfiability (SAT) problems and optimization problems that are transformed to SAT. It is regarded as a promising algorithm, especially when its hardware architecture is deployed on the Internet-of-Things (IoT) and embedded systems applications due to its inherent parallelism and excellent solution search performance. The key feature of this algorithm is to leverage fluctuations for efficiently searching a solution. However, no prior works sufficiently explored the suitable architecture structure nor the effective fluctuation parameters to realize the hardware solver. This paper intensively studies multiple architectures to implement the AmoebaSAT hardware solver and the effects of fluctuations, which are realized by pseudo random number generators on a digital device. By exhaustively examining the combinations of fluctuation parameters, we provide insightful findings and discussions on the efficient architecture and parameters from the algorithmic perspectives.

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“…Such effects can be utilized not only for AmoebaSAT but also for other algorithms that require fluctuations, such as stochastic computing. 26)…”

Section: Results Of Ev2mentioning

confidence: 99%

Section: Motivationmentioning

confidence: 86%

Digital bio-inspired satisfiability solver leveraging fluctuations

Hara-Azumi

Takeuchi

Hara

et al. 2020

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

show abstract

“…One of the major short-comings of SC is the hardware area costs required for stochastic bit-stream generation (SBG). CMOS based random number generators (RNGs) are typically linear shift feedback registers (LSFR) [15] or ring oscillators [16]. When these circuits are used in SC-based networks, the hardware for SBG can consume up to 80% of the total circuit area and 80% of the total energy consumption [8].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Computing Scheme of Embedding Random Bit Generation and Processing in Computational Random Access Memory (SC-CRAM)

Zink

Zabihi

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Stochastic computing (SC) has emerged as a promising solution for performing complex functions on large amounts of data to meet future computing demands. However, the hardware needed to generate random bit-streams using conventional CMOS based technologies drastically increases the area and delay cost. Area costs can be reduced using spintronics based RNGs, however, this will not alleviate the delay costs since stochastic bit generation is still performed separately from the computation. In this paper, we present an SC method of embedding stochastic bit generation and processing in a computational random access memory array, which we refer to as SC-CRAM. We demonstrate that SC-CRAM is a resilient and low cost method for image processing, Bayesian inference systems, and Bayesian belief networks.

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“…Despite the promising prospects of SC, the hardware costs for generating stochastic bits using modern CMOS technology make the realization of SC in future technology unfeasible. Generating stochastic bits using CMOS-based platforms are typically done using linear feedback shift registers (LFSRs) [31] or ring oscillator-based circuits [8]. In some cases, the circuits required for generating stochastic bitstreams take 80% of the total circuit and 80% of the total energy consumption [10].…”

Section: Introductionmentioning

confidence: 99%

Review of Magnetic Tunnel Junctions for Stochastic Computing

Zink

Wang

2022

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Modern computing schemes require large circuit areas and large energy consumption for neuromorphic computing applications, such as recognition, classification, and prediction. This is because these tasks require parallel processing on large datasets. Stochastic computing (SC) is a promising alternative to conventional binary computing schemes due to its low area cost, low processing power, and robustness to noise. However, the large area and energy costs for random number generation with CMOS-based circuits make SC impractical for most hardware implementations. For this reason, beyond-CMOS approaches to random number generation have been investigated in recent years. Spintronics is one of the most promising approaches due to the intrinsic stochasticity of the magnetic tunnel junction (MTJ). In this review article, we provide an overview of the literature published in recent years investigating the tunable, intrinsic stochasticity of MTJs and proposing practical methods for random number generation using spintronic hardware.INDEX TERMS Magnetic tunnel junctions (MTJs), random number generators (RNGs), spintronic devices, stochastic-bit generators (SBGs), stochastic computing (SC).

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Generating Stochastic Bitstreams

Cited by 5 publications

References 26 publications

Digital bio-inspired satisfiability solver leveraging fluctuations

Digital bio-inspired satisfiability solver leveraging fluctuations

A Stochastic Computing Scheme of Embedding Random Bit Generation and Processing in Computational Random Access Memory (SC-CRAM)

Review of Magnetic Tunnel Junctions for Stochastic Computing

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