Low latency computing for time stretch instruments
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 di...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | JPhys Photonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7647/acff54 |
| _version_ | 1797655896262705152 |
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| author | Tingyi Zhou Bahram Jalali |
| author_facet | Tingyi Zhou Bahram Jalali |
| author_sort | Tingyi Zhou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T17:21:29Z |
| format | Article |
| id | doaj.art-42f989456849458a8e9e69d7d19216c4 |
| institution | Directory Open Access Journal |
| issn | 2515-7647 |
| language | English |
| last_indexed | 2024-03-11T17:21:29Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Photonics |
| spelling | doaj.art-42f989456849458a8e9e69d7d19216c42023-10-19T11:48:39ZengIOP PublishingJPhys Photonics2515-76472023-01-015404500410.1088/2515-7647/acff54Low latency computing for time stretch instrumentsTingyi Zhou0https://orcid.org/0000-0002-8192-5114Bahram Jalali1https://orcid.org/0000-0002-0169-8231Department of Electrical and Computer Engineering, UCLA , Los Angeles, CA 90095, United States of AmericaDepartment of Electrical and Computer Engineering, UCLA , Los Angeles, CA 90095, United States of AmericaTime 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.https://doi.org/10.1088/2515-7647/acff54nonlinear Schrödinger Kerneltime stretchfemtosecond instrumentmachine learninghardware accelerationartificial intelligence |
| spellingShingle | Tingyi Zhou Bahram Jalali Low latency computing for time stretch instruments JPhys Photonics nonlinear Schrödinger Kernel time stretch femtosecond instrument machine learning hardware acceleration artificial intelligence |
| title | Low latency computing for time stretch instruments |
| title_full | Low latency computing for time stretch instruments |
| title_fullStr | Low latency computing for time stretch instruments |
| title_full_unstemmed | Low latency computing for time stretch instruments |
| title_short | Low latency computing for time stretch instruments |
| title_sort | low latency computing for time stretch instruments |
| topic | nonlinear Schrödinger Kernel time stretch femtosecond instrument machine learning hardware acceleration artificial intelligence |
| url | https://doi.org/10.1088/2515-7647/acff54 |
| work_keys_str_mv | AT tingyizhou lowlatencycomputingfortimestretchinstruments AT bahramjalali lowlatencycomputingfortimestretchinstruments |