论文标题
非线性Schrödinger内核用于机器学习推理中的硬件加速度
Nonlinear Schrödinger Kernel for hardware acceleration in machine learning inference
论文作者
论文摘要
替代的机器学习方法在计算上具有低潜伏期的计算轻度，并且只需要与小型培训数据集一起使用，而对于无法满足计算能力和大型培训数据的深度学习方法需求的应用程序。我们表明，使用非线性光学动力学将数据的光谱映射到飞秒的光学脉冲上，并投影到隐式，更高的尺寸空间中提高了准确性，并减少了数据分类的延迟。该方法通过各种数据集的分类来验证，包括脑内压，癌细胞成像，口语识别以及非线性分类的经典独家或基准。通过播种导致许多引人入胜的自然现象的非线性动力学，例如光流氓波，并在使用光分类器处理输出之前，可以证明这一概念。与众所周知的数值技术进行了定量比较，以提供对这种物理技术的见解。使用时间拉伸数据采集证明了单发操作。
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.
