光子学
光子晶体
计算机科学
带宽(计算)
可扩展性
纳米光子学
人工神经网络
超立方体
拓扑(电路)
电子工程
光电子学
物理
电信
人工智能
电气工程
并行计算
工程类
数据库
作者
Aashu Jha,Chaoran Huang,deLima, Thomas Ferriera,Hsuan-Tung Peng,Bhavin J. Shastri,Paul R. Prucnal
标识
DOI:10.1109/jstqe.2022.3179983
摘要
The bandwidth and energy demands of neural networks has spurred tremendous interest in developing novel neuromorphic hardware, including photonic integrated circuits. Although an optical waveguide can accommodate hundreds of channels with THz bandwidth, the channel count of photonic systems is always bottlenecked by the devices within. In WDM-based photonic neural networks, the synapses, i.e. network interconnections, are typically realized by microring resonators (MRRs), where the WDM channel count ( $N$ ) is bounded by the free-spectral range of the MRRs. For typical Si MRRs, we estimate $N \leq 30$ within the C-band. This not only restrains the aggregate throughput of the neural network but also makes applications with high input dimensions unfeasible. We experimentally demonstrate that photonic crystal nanobeam based synapses can be FSR-free within C-band, eliminating the bound on channel count. This increases data throughput as well as enables applications with high-dimensional inputs like natural language processing and high resolution image processing. In addition, the smaller physical footprint of photonic crystal nanobeam cavities offers higher tuning energy efficiency and a higher compute density than MRRs. Nanophotonic cavity based synapse thus offers a path towards realizing highly scalable photonic neural networks.
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