PIC: Hybrid Silicon Electronic-Photonic Integrated Neuromorphic Networks

PIC：混合硅电子-光子集成神经形态网络

• 批准号: 1810282
• 负责人: Stefan Preble
• 金额: $ 42.27万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1810282&HistoricalAwards=false
• 关键词: PIC; Hybrid; Silicon; Electronic; Photonic

Neuromorphic computing is a sub-field of artificial intelligence that implements physical architectures inspired by the learning processes in the brain. There have been significant efforts to realize neural network architectures using electronic integrated circuit technology. However, electronic-only hardware is not suitable for high bandwidth applications critical to a modern information world. In contrast, the internet is powered by photonic technologies (lasers, electro-optic modulator and photodetectors) because of light's high bandwidth, speed and low energy consumption. Consequently, this project aims to realize high performance neural networks that utilize light. These photonic neural networks will be integrated on a photonic chip in order to realize scalable and efficient architectures. However, in order to build neural networks that transcend today's state-of-art, it is necessary to also leverage electronics due to the challenges surrounding photonic memory and amplification, both of which are key to realizing a general purpose neural network. This hybrid approach, where electronics and photonics would be integrated together, enables the investigation of the broadest class of problems. In addition to these research aims, this project is an interdisciplinary activity that will provide technical training for future science and engineering professionals. There will be outreach activities that bring the research to K-12, undergraduate, and graduate students. Students from underrepresented backgrounds will be actively engaged by providing lab visits with hands-on activities. Lastly, the education initiatives of AIM Photonics Academy will be leveraged to disseminate the research in the project. Overall this project will impact the broader community with applications in autonomous systems, vision systems, information networks, cybersecurity, robotics and other high bandwidth applications.This project aims to address two fundamental questions, i) How can photonics maximize functionality in the compute domains?, ii) What neuromorphic algorithms can solve a broad class of problems using photonics?The overall goal of this project is to demonstrate hybrid silicon electronic-photonic integrated neuromorphic networks. The proposed paradigm leverages the power of optical interference to realize high performance neuromorphic computing networks. Photonic implementations of neural networks offer the inherent advantage that light can easily perform computational tasks that are traditionally challenging to do in electronic-only implementations (e.g. a Fourier transform can be done optically by simply passing light through a lens). The underlying integrated photonic-electronic network proposed here utilizes a Multimode interference coupler as a neural core (Neuro-MMI) in order to realize interference between multiple inputs and outputs in a compact footprint. The principal investigators propose to realize reconfigurability of the weights in the neural network wrapped around the MMI core. The Neuro-MMI core will be integrated with optoelectronic nonlinear thresholding circuits (along with electronic memory) to realize different classes of neural networks (feed forward neural networks and recurrent neural networks). Active Neuro-MMI's will be studied to realize on-chip learning and new learning rules will be investigated that are inherent for these topologies. Furthermore, the use of wavelength division multiplexing will be explored to achieve dense connectivity and parallelism in order to maximize the performance of the networks. One unique feature of the proposed hybrid photonic-electronic network is the reconfigurability to switch between feed forward and recurrent neural networks on a single chip.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经形态计算是人工智能的一个子领域，它实现了受大脑学习过程启发的物理架构。 已经有显著的努力来使用电子集成电路技术实现神经网络架构。 然而，纯电子硬件不适合于对现代信息世界至关重要的高带宽应用。相比之下，互联网是由光子技术（激光器，电光调制器和光电探测器）提供动力的，因为光的高带宽，速度和低能耗。因此，该项目旨在实现利用光的高性能神经网络。这些光子神经网络将被集成在一个光子芯片上，以实现可扩展和高效的架构。 然而，为了构建超越当今最先进水平的神经网络，由于围绕光子存储器和放大的挑战，还必须利用电子器件，这两者都是实现通用神经网络的关键。这种将电子学和光子学集成在一起的混合方法，能够对最广泛的问题进行研究。 除了这些研究目标，该项目是一个跨学科的活动，将为未来的科学和工程专业人员提供技术培训。 将有推广活动，使研究K-12，本科生和研究生。来自代表性不足的背景的学生将通过提供实验室参观与实践活动积极参与。 最后，AIM Photonics Academy的教育计划将被用来传播该项目的研究成果。总的来说，该项目将影响更广泛的社区与应用在自主系统，视觉系统，信息网络，网络安全，机器人和其他高带宽应用。该项目旨在解决两个基本问题，i）光子学如何最大限度地提高计算领域的功能？，ii）什么样的神经形态算法可以使用光子学解决广泛的问题？这个项目的总体目标是展示混合硅电子-光子集成神经形态网络。所提出的范例利用光干涉的力量来实现高性能的神经形态计算网络。神经网络的光子实现提供了固有的优点，即光可以容易地执行传统上在仅电子实现中具有挑战性的计算任务（例如，傅立叶变换可以通过简单地使光穿过透镜来光学地完成）。这里提出的底层集成光电网络利用多模干扰耦合器作为神经核心（Neuro-MMI），以便在紧凑的占地面积中实现多个输入和输出之间的干扰。主要研究人员建议在MMI核心周围的神经网络中实现权重的可重构性。Neuro-MMI核心将与光电非线性阈值电路（沿着电子存储器）集成，以实现不同类别的神经网络（前馈神经网络和递归神经网络）。 有源神经MMI的将被研究，以实现片上学习和新的学习规则将被调查，这些拓扑结构是固有的。此外，还将探索使用波分复用技术来实现密集连接和并行，以最大限度地提高网络性能。建议的混合光电网络的一个独特的功能是可重构性之间切换前馈和递归神经网络在一个单一的chip.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Massively scalable wavelength diverse integrated photonic linear neuron

大规模可扩展的波长多样化集成光子线性神经元

• DOI: 10.1088/2634-4386/ac8ecc
• 发表时间: 2022
• 期刊: Neuromorphic Computing and Engineering
• 作者: van Niekerk, Matthew;Rizzo, Anthony;Rubio, Hector;Leake, Gerald;Coleman, Daniel;Tison, Christopher;Fanto, Michael;Bergman, Keren;Preble, Stefan
• 通讯作者: Preble, Stefan

Approximating large scale arbitrary unitaries with integrated multimode interferometers

• DOI: 10.1117/12.2523581
• 发表时间: 2019-05
• 作者: Matthew van Niekerk;J. Steidle;Gregory A. Howland;M. Fanto;Nicholas Soures;F. Zohora;D. Kudithipudi;S. Preble

Two-dimensional extreme skin depth engineering for CMOS photonics

CMOS 光子学的二维极限趋肤深度工程

• DOI: 10.1364/josab.416848
• 发表时间: 2021
• 期刊: Journal of the Optical Society of America B
• 作者: van Niekerk, Matthew;Jahani, Saman;Bickford, Justin;Cho, Pak;Anderson, Stephen;Leake, Gerald;Coleman, Daniel;Fanto, Michael L.;Tison, Christopher C.;Howland, Gregory A.
• 通讯作者: Howland, Gregory A.