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Temperature perturbation effects on image processing dedicated stochastic artificial neural networks

机译：温度扰动对图像处理专用随机人工神经网络的影响

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Abstract: The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, especially the Boltzmann Machine, show a number of many attractive features. Recent studies on artificial models point out that classification is their most successful application field, and that real pattern recognition tasks, and especially image processing by artificial neural networks will require large networks. All of the presented implementations of ANN are supposed to be working in ideal conditions but real applications are subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a firth order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a Boltzmann Machine model's behavior with physical temperature perturbation. The relation between the T parameter of the Boltzmann Machine model and the physical temperature of circuit has been established. Simulation results are presented and temperature effects compensation is discussed. !25
机译：摘要：人工神经网络（ANN）作为CMOS模拟集成电路的实现表现出几个吸引人的特征。随机模型，尤其是玻尔兹曼机，显示出许多吸引人的特征。对人工模型的最新研究指出，分类是其最成功的应用领域，并且真实的模式识别任务，尤其是人工神经网络的图像处理将需要大型网络。所有提出的ANN实施都应该在理想条件下工作，但实际应用会受到干扰。对于ANN的数字实现，扰动效应可以以近似的第一阶忽略。但是对于模拟和混合数字/模拟实现情况，带有扰动条件的神经网络的行为分析是不可避免的。不幸的是，很少有论文用扰动或其局限性来分析模拟神经网络的行为。在本文中，我们对带有物理温度扰动的玻尔兹曼机模型的行为进行了分析。建立了玻尔兹曼模型的T参数与电路物理温度之间的关系。给出了仿真结果并讨论了温度效应补偿。 ！25

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