Neuromorphic photonics: 2D or not 2D?
Neuromorphic photonics: 2D or not 2D?
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DOI：
10.1063/5.0047946
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发表时间：
2021-05-28
影响因子：
3.2
通讯作者：
Pleros, N.
中科院分区：
文献类型：
--
作者：
Stabile, R.;Dabos, G.;Pleros, N.
计算行业正在迅速从编程领域转向学习领域，冯·诺依曼架构的统治地位在多年的统治之后开始消退。非冯·诺依曼架构的新计算范式已经开始导致新兴的基于人工神经网络（ANN）的模拟电子人工智能（AI）芯片组的开发，其具有显著的能效。然而，由于电阻器-电容器（RC）寄生效应，电子处理元件的尺寸和能量优势自然地被电路内部的电子互连的速度和功率限制抵消。神经形态光子学作为一个新的研究领域应运而生，其目标是将光子电路的高带宽和低能量互连证书转移到神经形态平台领域。神经形态光子学的高潜力及其在更高的神经元密度的数量级上的fJ/多光子累积能量效率的良好承诺需要沿着整个技术栈的一些突破沿着，面临着在选择用于加权和激活功能的最佳光子材料平台以及将其转换为共同集成的光子计算引擎方面的重大进步。在本文中，我们分析了神经形态计算和现有光子集成技术的现状，并提出了一种新型的三维计算单元，该单元具有紧凑性，可编程效率和无损互连性，可以预见到可扩展的计算AI芯片组在计算速度和能源效率方面优于电子产品，以塑造神经形态计算的未来。
The computing industry is rapidly moving from a programming to a learning area, with the reign of the von Neumann architecture starting to fade, after many years of dominance. The new computing paradigms of non-von Neumann architectures have started leading to the development of emerging artificial neural network (ANN)-based analog electronic artificial intelligence (AI) chipsets with remarkable energy efficiency. However, the size and energy advantages of electronic processing elements are naturally counteracted by the speed and power limits of the electronic interconnects inside the circuits due to resistor-capacitor (RC) parasitic effects. Neuromorphic photonics has come forward as a new research field, which aims to transfer the well-known high-bandwidth and low-energy interconnect credentials of photonic circuitry in the area of neuromorphic platforms. The high potential of neuromorphic photonics and their well-established promise for fJ/Multiply-ACcumulate energy efficiencies at orders of magnitudes higher neuron densities require a number of breakthroughs along the entire technology stack, being confronted with a major advancement in the selection of the best-in-class photonic material platforms for weighting and activation functions and their transformation into co-integrated photonic computational engines. With this paper, we analyze the current status in neuromorphic computing and in available photonic integrated technologies and propose a novel three-dimensional computational unit which, with its compactness, ultrahigh efficiency, and lossless interconnectivity, is foreseen to allow scalable computation AI chipsets that outperform electronics in computational speed and energy efficiency to shape the future of neuromorphic computing.