Deep Binarized Photonic Reservoir Computing for Ultrafast Multimedia Signal Processing
This work addresses the need for ultrafast, scalable hardware for real-time multimedia processing, though it is an incremental step combining existing techniques.
The authors present a deep photonic reservoir computing system using binary optical modulation and random scattering, achieving Gb/s processing rates and state-of-the-art performance on video, image, and speech recognition tasks.
We present a deep photonic neural network architecture based on ultrafast binary optical modulation from a digital micro-mirror device (DMD), optical scattering in random medium, high-speed photodetection with a CMOS sensor, and time-multiplexed deep layer structure. Operating at Gigabit-per-second (Gb/s) processing rates, our system based on the reservoir computing (RC) framework achieves state-of-the-art performance across various multimedia tasks, including video, image and speech recognition. We show that the careful optimization of key physical intra- and inter-layer hyper-parameters can significantly enhance the deep photonic RC system ability to extract relevant temporal and spatial features via balancing memory retention and dynamical response of individual layers. This approach paves the way for highly scalable hierarchical photonic reservoir computing systems for high-throughput real-time multimedia signal processing.