降低量化水平的混合信号光学卷积神经网络训练
Training of mixed-signal optical convolutional neural network with reduced quantization level
摘要：
采用模拟矩阵乘法加速器的混合信号人工神经网络（ANN）可以实现更高的速度和更高的功率效率。虽然模拟计算容易受到噪声和设备缺陷的影响，但由于人工神经网络的鲁棒性，各种模拟计算范式被认为是解决机器学习应用中不断增长的计算需求的有前途的解决方案。这种鲁棒性已经在低精度、定点ANN模型中进行了探索，这些模型已经被证明在数字计算机上成功地压缩了ANN模型的大小。然而，这些有希望的结果和网络训练算法不能轻易地迁移到模拟加速器。原因是数字计算机通常携带具有较高位宽的中间结果，尽管每个ANN层的输入和权重具有较低的位宽;而模拟中间结果具有较低的精度，类似于具有降低的量化水平的数字信号。在这里，我们报告了一种训练方法的混合信号人工神经网络与两种类型的错误，在其模拟信号，随机噪声和确定性的错误（失真）。实验结果表明，在噪声水平为理想量化步长的50%的情况下，用该方法训练的混合信号神经网络可以达到与理想量化步长相当的分类精度。我们已经在基于衍射光学的混合信号光学卷积神经网络上证明了这种训练方法。
英文摘要：
Mixed-signal artificial neural networks (ANNs) that employ analog matrix-multiplication accelerators can achieve higher speed and improved power efficiency. Though analog computing is known to be susceptible to noise and device imperfections, various analog computing paradigms have been considered as promising solutions to address the growing computing demand in machine learning applications, thanks to the robustness of ANNs. This robustness has been explored in low-precision, fixed-point ANN models, which have proven successful on compressing ANN model size on digital computers. However, these promising results and network training algorithms cannot be easily migrated to analog accelerators. The reason is that digital computers typically carry intermediate results with higher bit width, though the inputs and weights of each ANN layers are of low bit width; while the analog intermediate results have low precision, analogous to digital signals with a reduced quantization level. Here we report a training method for mixed-signal ANN with two types of errors in its analog signals, random noise, and deterministic errors (distortions). The results showed that mixed-signal ANNs trained with our proposed method can achieve an equivalent classification accuracy with noise level up to 50% of the ideal quantization step size. We have demonstrated this training method on a mixed-signal optical convolutional neural network based on diffractive optics.
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