Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing

Morro, Antoni; Canals, Vincent; Oliver, Antoni; Alomar, Miquel L.; Rosselló, Josep L.

doi:10.1371/journal.pone.0124176

Cited by 24 publications

(8 citation statements)

References 26 publications

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“…Many of these emerging ideas, such as stochastic computing [2][3][4][5][6] and some brain-inspired (or neuromorphic) schemes [7][8][9] , require a large quantity of random numbers. However, the circuit area and the energy required to generate these random numbers are major limitations of such computing schemes.…”

Section: Introductionmentioning

confidence: 99%

“…More concerning, in stochastic computing architectures, random number generation is typically the dominant source of energy consumption, as the logic performed using the random bits is generally quite simple and efficient by principle. Many practical stochastic computing schemes therefore try to limit the reliance on expensive independent random bits using various techniques, including the sharing or reuse of random bits [10][11][12] . However, such tricks limit the capabilities of stochastic computing to small tasks, as they introduce correlations between signals.…”

Section: Introductionmentioning

confidence: 99%

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Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

et al. 2017

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Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is prohibitive for these computing schemes. In this paper, we introduce random number bit generation based on specific nanodevices: superparamagnetic tunnel junctions. We experimentally demonstrate high quality random bit generation that represents orders-of-magnitude improvements in energy efficiency compared to current solutions. We show that the random generation speed improves with nanodevice scaling, and investigate the impact of temperature, magnetic field and crosstalk. Finally, we show how alternative computing schemes can be implemented using superparamagentic tunnel junctions as random number generators. These results open the way for fabricating efficient hardware computing devices leveraging stochasticity, and highlight a novel use for emerging nanodevices.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

et al. 2017

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“…This approach, namely Stochastic Computing (SC) or Stochastic Logic, makes a trade off between calculation time and accuracy. This approach has been successfully applied in fields as diverse as neural network implementation [25,26], data mining [27], data compression [28], or mathematical calculations (FFT) [29], control [30], or even A/D conversion [31], among others. Indicative of its advantages, it allows for a high reduction in the number of components, thus reducing the power required to run the circuit.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Computing Implementation of Chaotic Systems

2021

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An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

show abstract

“…This approach, namely Stochastic Computing (SC) or Stochastic Logic, makes a trade off between calculation time and accuracy. This approach has been successfully applied in fields as diverse as neural network implementation [25,26], data mining [27], data compression [28], or mathematical calculations (FFT) [29], control [30], or even A/D conversion [31], among others. Indicative its advantages, it allows for a high reduction in the number of components, thus reducing the power required to run the circuit.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Computing Implementation of Chaotic Systems

Camps¹,

Stavrinides²,

Picos³

2021

Preprint

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An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

show abstract

Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing

Cited by 24 publications

References 26 publications

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing

Stochastic Computing Implementation of Chaotic Systems

Stochastic Computing Implementation of Chaotic Systems

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