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2 April 2020 Opportunities of silicon photonics for calculation and machine learning applications (Conference Presentation)
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Computing enabled by electronics has been improved so extensively as to make machine learning algorithms such as deep neural network so powerful than ever before. Data communications enabled by optics have been one of the cornerstones of the modern society built upon Internet. The rapidly increasing demands for communication bandwidth due to numerous emerging applications such as AI-based cloud computing have significantly increased the bisection bandwidth of intra-datacenter networks. This trend will necessitate the optics-electronics co-packaging on board, which can only be realized by the substantial development of integrated photonics such as silicon photonics. This opportunity, making optics and electronics so close to each other, will in turn offer a chance to reconsider building EO-hybrid computational and intelligent systems. Although optical computing and optical neural networks have been proposed since a while ago, recent demonstrations of deep neural networks implemented on silicon photonics reactivate the study of exploiting photonics for machine learning. The current program-based deep neural networks are powerful, but consume huge computational resources. Suppose that just optical propagation in compact photonic chips could realize similar functions, it would greatly decrease the power and latency. Even though the scalability and the integration of nonlinear activation functions on photonic chips are still challenging, for some applications such as classifier and digit recognition, photonics systems could be viable and beneficial from such aspects. This talk will introduce our efforts to develop the topology concepts, algorithms, and applications in order to implement silicon photonics for calculation and machine learning applications. First, a high-bit reconfigurable DAC based on generic photonic circuits will be presented. Second, without using any nonlinear activation functions or building deep neural networks, we demonstrate a photonic classifier based on only linear optical components. At last, we will show several ways of implementing silicon photonic circuit to recognize digits.
Conference Presentation
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Guangwei Cong, Noritsugu Yamamoto, Takashi Inoue, Yuriko Maegami, Morifumi Ohno, Makoto Okano, Shu Namiki, and Koji Yamada "Opportunities of silicon photonics for calculation and machine learning applications (Conference Presentation)", Proc. SPIE 11364, Integrated Photonics Platforms: Fundamental Research, Manufacturing and Applications, 1136409 (2 April 2020);

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