用于机器学习推理中硬件加速的非线性薛定谔核
Nonlinear Schrödinger Kernel for hardware acceleration in machine learning inference
摘要：
对于无法满足深度学习方法对计算能力和大型训练数据的永不满足的需求的应用程序，需要计算量小、延迟低且仅能处理小训练数据集的替代机器学习方法。我们表明，光谱映射到飞秒光脉冲的数据和投影到一个隐式的，更高的维度空间，使用非线性光学动力学提高了准确性，并减少了几个数量级的数据分类的延迟。该方法通过各种数据集的分类进行了验证，包括脑颅内压，癌细胞成像，口语数字识别，以及经典的非线性分类的异或基准。这个概念是通过在用光分类器处理输出之前，用数据播种非线性动力学来证明的，非线性动力学是许多迷人的自然现象的原因，例如光学流氓波。与一个众所周知的数值技术的定量比较是用来提供洞察这种物理技术。使用时间拉伸数据采集演示了单次发射操作。
英文摘要：
Alternative machine learning approaches that are computationally light with low latency and can work with only a small training dataset are needed for applications where the insatiable demand of deep learning methods for computing power and large training data cannot be met. We show that spectral mapping of data onto femtosecond optical pulses and a projection into an implicit, higher dimensional space using nonlinear optical dynamics increases the accuracy and reduces the latency in data classification by several orders of magnitude. The approach is validated by the classification of various datasets, including brain intracranial pressure, cancer cell imaging, spoken digit recognition, and the classic exclusive OR benchmark for nonlinear classification. The concept is demonstrated by seeding the nonlinear dynamics that are responsible for many fascinating natural phenomena, such as optical rogue waves, with the data before processing the output with a light classifier. A quantitative comparison with a well-known numerical technique is used to provide insight into this physical technique. Single-shot operation is demonstrated using time stretch data acquisition.
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