Scalable Ising Computer Based on Ultra-Fast Field-Free Spin Orbit Torque Stochastic Device with Extreme 1-Bit Quantization

Yin, Jialiang; Liu, Yu; Zhang, Bolin; Du, Ao; Gao, Tianqi; Ma, Xiangyue; Dong, Yiyang; Bai, Yanling; Lu, Shengfu; Zhuo, Yudong; Huang, Yan; Cai, Wenlong; Zhu, Daoqian; Shi, Kewen; Cao, Kaihua; Zhang, Deming; Zeng, Lang; Zhao, Weisheng

doi:10.1109/iedm45625.2022.10019520

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…[18,76,77] (STT) and in Ref. [78] (SOT). While many other implementations of p-bits are possible, from molecular nanomagnets [79] to diffusive memristors [80], RRAM [81], perovskite nickelates [82] and others, two additional advantages of the MRAM-based p-bits are the proven manufacturability (up to billion bit densities) and the amplification of room temperature noise.…”

Section: Mixed-signalmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

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

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The transistor celebrated its 75 th birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

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Section: Mixed-signalmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

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

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“…Yin et al demonstrated the in-plane superparamagnetic MTJ devices, the schematic of the stack can be seen in figure 18 [148]. [155]. The ultra-fast field-free p-bit tunable by SOT effect is proposed and fabricated.…”

Section: Stochastic Mtjsmentioning

confidence: 99%

Tunneling magnetoresistance materials and devices for neuromorphic computing

et al. 2023

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Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as Spike-Timing Dependent Plasticity(STDP) and Leaky Integrate-Fire(LIF), but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs' bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the Internet of Things.

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“…In our exploration, the SOT-MTJ devices serve the elementary function of a Bernoulli true random number generator (TRNG) for stochastic sampling, acting as a binary TRNG with a tunable probability P or (1 – P ) to sample 1 or 0, respectively. Typically, MTJ-based TRNGs can be categorized into two types: low-barrier ,− and high-barrier systems (Figure b). The former features a barrier comparable to k B T (low thermal stability factor), and hence, its magnetic configuration or junction resistance can stochastically flip between the P and AP states due to thermal fluctuations.…”

mentioning

confidence: 99%

Restricted Boltzmann Machines Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions

Li,

Wan,

Zhang

et al. 2024

Nano Lett.

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Artificial intelligence has surged forward with the advent of generative models, which rely heavily on stochastic computing architectures enhanced by true random number generators with adjustable sampling probabilities. In this study, we develop spin−orbit torque magnetic tunnel junctions (SOT-MTJs), investigating their sigmoid-style switching probability as a function of the driving voltage. This feature proves to be ideally suited for stochastic computing algorithms such as the restricted Boltzmann machines (RBM) prevalent in pretraining processes. We exploit SOT-MTJs as both stochastic samplers and network nodes for RBMs, enabling the implementation of RBM-based neural networks to achieve recognition tasks for both handwritten and spoken digits. Moreover, we further harness the weights derived from the preceding image and speech training processes to facilitate cross-modal learning from speech to image generation. Our results clearly demonstrate that these SOT-MTJs are promising candidates for the development of hardware accelerators tailored for Boltzmann neural networks and other stochastic computing architectures.

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Scalable Ising Computer Based on Ultra-Fast Field-Free Spin Orbit Torque Stochastic Device with Extreme 1-Bit Quantization

Cited by 5 publications

References 7 publications

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Tunneling magnetoresistance materials and devices for neuromorphic computing

Restricted Boltzmann Machines Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions

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