时间拉伸仪器的低延迟计算
Low Latency Computing for Time Stretch Instruments
AI总结：
针对时间拉伸仪器数据采集速率与神经网络推理时间尺度不匹配的问题，提出利用飞秒激光脉冲的数字相位编码来有效调整和训练非线性薛定谔核计算的光学核，从而在光域内完成低级计算任务并降低数据分类错误率。
摘要：
时间拉伸仪器在发现光学 rogue 波等单次超快现象方面取得了异常成功，并带来了创纪录速度的显微成像、光谱学、激光雷达等。这些仪器将超快事件编码到飞秒脉冲的频谱中，然后利用群速度色散扩展数据的时间尺度。它们产生高达 Tbit 每秒的数据，是深度学习网络的理想伙伴，因为深度学习网络由于其固有的复杂性，需要大型数据集进行训练。然而，毫秒级神经网络的推理时间尺度比时间拉伸仪器的数据采集速率长几个数量级。这强调了需要探索一些方法，使得一些较低级别的计算任务可以在数据仍处于光域时完成。非线性薛定谔核计算解决了这一困境。它利用光学非线性将数据映射到一个新的域，在该域中分类精度得到提高，而无需增加数据维度。该技术的一个局限性是固定的光学传递函数，这阻碍了训练和泛化能力。在这里，我们展示了通过利用飞秒激光脉冲的数字相位编码，可以有效地调整和训练光学核，从而降低数据分类的错误率。
英文摘要：
Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
