In particular, on-chip photonic neural networks offer the promise of ultralow latency processing 23 but usually suffer from physical size constraints arising from the difficulty of high-density photonic integration. Some of these processors enable direct image acquisition without the use of image sensors and subsequent optical processing 14, 22, 23. Photonic neural network processors have great potential for accelerating image processing 14, 16, 22, 23, 24, 25, 26, 27. The electrical domain conversion and memory accesses required for large amounts of data are significant bottlenecks that hinder the speed of image processing (Fig. In such systems, the spatial information acquired by an image sensor is converted into the electrical domain in a digital format, and large amounts of memory are required for data storage.
This limitation becomes particularly severe when image sensors with numerous pixels are employed.
However, when photonic processing units handle signals acquired by sensing devices, the overall processing speed is essentially limited by the data acquisition speed of the sensing devices and the transfer to the processing units. Such photonic approaches hold promise for accelerating signal preprocessing from sensing units, thereby alleviating the computational burden typically borne by electronic postprocessing units. Photonic computing substrates have been predominantly used to process optical analog signals and play an essential role in the interface between the physical world and the digital domain 18, 22. Recent studies have revealed the potential for overcoming major bottlenecks in electronic computing, suggesting that ultrahigh-speed computing with low energy consumption can be achieved 16, 20, 21. Among them, photonic computing has attracted considerable attention owing to recent developments in photonic integration and optical communication technologies 6, 13, 14, 15, 16, 17, 18, 19. Rapid advances in information technologies, particularly in fields such as machine learning, have generated an escalating demand for innovative computing hardware and concepts 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. The proposed approach can be extended to diverse applications, including target tracking, flow cytometry, and imaging of sub-nanosecond phenomena. Furthermore, we demonstrate that this approach can also be used for high-speed learning-based imaging. We combine it with a photonic reservoir computer and demonstrate that this approach is capable of dynamic image recognition at gigahertz rates.
CMOS DIGITAL ISOLATOR SERIAL
The drawback of the time-domain serial operation can be mitigated using ultrahigh-speed data acquisition based on gigahertz-rate speckle projection. Thus, large-scale processing is enabled even when using a small photonic processor with limited input/output channels. Here, we propose a photonic time-domain image processing approach, where real-world visual information is compressively acquired through a single input channel. On-chip photonic neural network processors have the potential to speed up image processing, but their scalability is limited in terms of the number of input/output channels because high-density integration is challenging. High-speed image processing is essential for many real-time applications.
0 kommentar(er)
