A compact model of stochastic switching in STT magnetic RAM for memory and computing

Carboni, Roberto; Vernocchi, E.; Siddik, Manzar; Harms, J.; Lyle, Andy; Sandhu, Gurtej; Ielmini, Daniele

doi:10.1109/nanoarch47378.2019.181290

Cited by 1 publication

(4 citation statements)

References 13 publications

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“…Note that the thermal model correctly predicts the linear behavior for slower t P . However, it cannot explain the deviation from the linear behavior at t P < 200 ns [20,27]. To this purpose, Fig.…”

Section: Modeling Stochastic Switching In Stt-mrammentioning

confidence: 97%

“…All tests are passed, thus supporting the feasibility of STT-based TRNG. [27] Set/reset switching variability in STT-MRAM can also be exploited towards stochastic spiking neurons (Fig. 5.8a) [27].…”

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

“…[27] Set/reset switching variability in STT-MRAM can also be exploited towards stochastic spiking neurons (Fig. 5.8a) [27]. In the proposed spiking neuron, a STT-MRAM cell is biased at constant voltage V A to induce stochastic set transition from the AP state.…”

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

“…The applicability of the model to computing with spiking neurons was demonstrated for analogue multiplication (stochastic computing) and SRDP-based [30] spiking neural network [27].…”

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

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Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

Carboni

2021

Special Topics in Information Technology

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With the ubiquitous diffusion of mobile computing and Internet of Things (IoT), the amount of data exchanged and processed over the internet is increasing every day, demanding secure data communication/storage and new computing primitives. Although computing systems based on microelectronics steadily improved over the past 50 years thanks to the aggressive technological scaling, their improvement is now hindered by excessive power consumption and inherent performance limitation associated to the conventional computer architecture (von Neumann bottleneck). In this scenario, emerging memory technologies are gaining interest thanks to their non-volatility and low power/fast operation. In this chapter, experimental characterization and modeling of spin-transfer torque magnetic memory (STT-MRAM) are presented, with particular focus on cycling endurance and switching variability, which both present a challenge towards STT-based memory applications. Then, the switching variability in STT-MRAM is exploited for hardware security and computing primitives, such as true-random number generator (TRNG) and stochastic spiking neuron for neuromorphic and stochastic computing.

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Section: Modeling Stochastic Switching In Stt-mrammentioning

confidence: 97%

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

“…The applicability of the model to computing with spiking neurons was demonstrated for analogue multiplication (stochastic computing) and SRDP-based [30] spiking neural network [27].…”

Section: Stochastic Stt Switching For Security and Computingmentioning

confidence: 99%

See 2 more Smart Citations

Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

Carboni

2021

Special Topics in Information Technology

View full text Add to dashboard Cite

show abstract

A compact model of stochastic switching in STT magnetic RAM for memory and computing

Cited by 1 publication

References 13 publications

Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

Characterization and Modeling of Spin-Transfer Torque (STT) Magnetic Memory for Computing Applications

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