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.

中文翻译：

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通过自旋轨道扭矩磁隧道结实现的受限玻尔兹曼机

随着生成模型的出现，人工智能蓬勃发展，生成模型严重依赖随机计算架构，而随机计算架构由具有可调采样概率的真正随机数生成器增强。在这项研究中，我们开发了自旋轨道扭矩磁隧道结（SOT-MTJ），研究了它们作为驱动电压函数的 S 型开关概率。事实证明，此功能非常适合随机计算算法，例如预训练过程中普遍存在的受限玻尔兹曼机 (RBM)。我们利用 SOT-MTJ 作为 RBM 的随机采样器和网络节点，从而实现基于 RBM 的神经网络来实现手写和口头数字的识别任务。此外，我们进一步利用从前面的图像和语音训练过程中得出的权重来促进从语音到图像生成的跨模态学习。我们的结果清楚地表明，这些 SOT-MTJ 是开发为玻尔兹曼神经网络和其他随机计算架构量身定制的硬件加速器的有希望的候选者。