节点文献
Integrated diffractive optical neural network with space-time interleaving
【摘要】 Integrated diffractive optical neural networks(DONNs) have significant potential for complex machine learning tasks with high speed and ultralow energy consumption. However, the on-chip implementation of a high-performance optical neural network is limited by input dimensions. In contrast to existing photonic neural networks, a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed, high-dimensional, and all-optical input signal modulation. To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation, an on-chip DONN with a designed footprint of 0.0945 mm2is proposed to resolve the vowel recognition task, reaching a computation speed of about 1.4 × 1013 operations per second and yielding an accuracy of 98.3% in numerical calculation. In addition, the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally. This method can realize the on-chip DONN with higher input dimension and lower energy consumption.
【Abstract】 Integrated diffractive optical neural networks(DONNs) have significant potential for complex machine learning tasks with high speed and ultralow energy consumption. However, the on-chip implementation of a high-performance optical neural network is limited by input dimensions. In contrast to existing photonic neural networks, a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed, high-dimensional, and all-optical input signal modulation. To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation, an on-chip DONN with a designed footprint of 0.0945 mm2is proposed to resolve the vowel recognition task, reaching a computation speed of about 1.4 × 1013 operations per second and yielding an accuracy of 98.3% in numerical calculation. In addition, the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally. This method can realize the on-chip DONN with higher input dimension and lower energy consumption.
【Key words】 integrated diffractive optical neural networks; machine learning; arrayed waveguides;
- 【文献出处】 Chinese Optics Letters ,中国光学快报(英文版) , 编辑部邮箱 ,2023年09期
- 【分类号】TP183
- 【下载频次】3