Ma Chonghuai, Van Kerrebrouck Joris, Deng Hong, Sackesyn Stijn, Gooskens Emmanuel, Bai Bing, Dambre Joni, Bienstman Peter
Opt Express. 2023 Oct 9;31(21):34843-34854. doi: 10.1364/OE.502354.
Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward readout system, which facilitates both rapid training and robust, fabrication variation-insensitive photonic integrated hardware implementation for real-time processing. We present our recent development of a fully-optical, coherent photonic reservoir chip integrated with an optical readout system, capitalizing on these benefits. Alongside the integrated system, we also demonstrate a weight update strategy that is suitable for the integrated optical readout hardware. Using this online training scheme, we successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps in real-time, all within the optical domain without excess delays.
由于其神经网络特性，集成光子储层计算已被证明能够解决不同的问题。光子储层计算相对于其他神经形态范式的一个关键优势是其直接的读出系统，这有利于快速训练以及实现对制造变化不敏感的稳健光子集成硬件，以进行实时处理。利用这些优势，我们展示了我们最近开发的一种集成光学读出系统的全光相干光子储层芯片。除了该集成系统，我们还展示了一种适用于集成光学读出硬件的权重更新策略。使用这种在线训练方案，我们成功地在光域内实时解决了20 Gbps的3位报头识别和延迟异或任务，且没有额外延迟。
