Emerging Artificial Neuron Devices for Probabilistic Computing.
Emerging Artificial Neuron Devices for Probabilistic Computing.
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用于概率计算的新兴人工神经元设备
DOI：
10.3389/fnins.2021.717947
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发表时间：
2021
影响因子：
4.3
通讯作者：
Zhuge F
中科院分区：
文献类型：
--
作者：
Li ZX;Geng XY;Wang J;Zhuge F
近几十年来，人工智能已相继应用于金融、商业等行业。然而，模仿高级大脑功能，如想象力和推理，带来了一些挑战，因为它们与生物神经元网络中的特定类型的噪声有关。基于限制玻尔兹曼机和贝叶斯推理的概率计算算法，使用硅电子学在模仿概率推理方面取得了显着进展。然而，由附加电路或算法产生的准随机噪声对硅电子学实现生物神经元系统的真正随机性提出了重大挑战。基于新兴器件的人工神经元，如具有固有随机性的忆阻器和铁电场效应晶体管，可以产生不确定的非线性输出尖峰，这可能是使机器学习更接近人脑的关键。在这篇文章中，我们提出了一个全面的审查，在新兴的随机人工神经元（SAN）的概率计算方面的最新进展。我们简要介绍了生物神经元，神经元模型和硅神经元，然后介绍了各种SAN的详细工作机制。最后，讨论了硅基和新兴神经元的优缺点，并对SAN的发展前景进行了展望。
In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.
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