Sensor fusion enabled by photonic neuromorphic computing using phase-change materials

使用相变材料的光子神经形态计算实现传感器融合

• 批准号: 2602943
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602943
• 关键词: Sensor; fusion; enabled; photonic; neuromorphic

The use of functional materials that can "accumulate" information to carry out both memory and computing tasks in-situ is a growing field. Using optical integration onto silicon chips provides a unique opportunity to combine the benefits of silicon scaling and the wavelength multiplexing of optics onto a single platform. In this proposal, we will significantly expand our initial work on carrying out non-von Neumann computations (such as Vector-Matrix Multiplication directly in hardware) to a larger matrix to prove a lab scale demonstration of the potential for such hardware in defence-relevant computing tasks. Phase-change devices, that exploit reversible cycling between amorphous and crystalline states in chalcogenide alloys such as Ge2Sb2Te5 (GST), have been with us for some time now, providing binary non-volatile memories in both the optical domain (e.g. DVD-RAM and Blu-Ray RW disks) and more recently the electrical domain (e.g. in the 3D-XPoint memory announced by Intel/Micron in 2015). Along with others, we have also recently shown that phase-change based devices can provide a number of important non-von Neumann computing/processing functionalities, namely: 1) By exploiting a multi-level memory mode of operation, phase-change devices can mimic the basic operation of a biological synapse.2) By operating in an 'energy' accumulation mode, phase-change devices can mimic the basic operation of a biological neuron.3) By exploiting the accumulation mode, phase-change devices can also provide a form of non-von Neumann computing in which memory and (arithmetic) processing are carried out simultaneously in the same device. In recent work (Li et al, Optica 7 (3), 218-225), we have shown the quantitative experimental differences between Silicon and Silicon Nitride waveguides, and demonstrated multibit optical writing on a SOI platform. In this PhD proposal we will build on this firm foundation of proven basic principles and develop (design, fabricate and test) phase-change-based computing primitives, specifically synapse and neuron mimics and (binary and multi-level) non-volatile memories and combine such primitives into non-von Neumann computing networks (architectures). We will use these to demonstrate proof-of-concept for defence-relevant computational tasks related to sensor processing and management.

使用能够“积累”信息的功能材料来原位执行记忆和计算任务是一个不断发展的领域。在硅芯片上使用光学集成提供了一个独特的机会，可以将硅缩放和光学器件的波长多路复用的好处联合收割机结合到单个平台上。在本提案中，我们将显著扩展我们在执行非冯·诺依曼计算（例如直接在硬件中进行向量矩阵乘法）方面的初步工作，以更大的矩阵来证明这种硬件在国防相关计算任务中的潜力的实验室规模演示。利用诸如Ge 2Sb 2 Te 5（GST）的硫属化物合金中的非晶态和晶态之间的可逆循环的相变器件已经与我们在一起一段时间了，在光学领域（例如DVD-RAM和蓝光RW盘）和最近的电学领域（例如在2015年由Intel/Micron宣布的3D-XPoint存储器中）中提供二进制非易失性存储器。沿着其他人，我们最近还表明，基于相变的设备可以提供许多重要的非冯·诺依曼计算/处理功能，即：1）通过利用多级存储器操作模式，相变设备可以模仿生物突触的基本操作。2）通过以“能量”累积模式操作，相变器件可以模拟生物神经元的基本操作。3）通过利用累积模式，相变器件还可以提供一种形式的非冯·诺依曼计算，其中在同一器件中同时执行存储器和（算术）处理。 在最近的工作中（Li等人，Optica 7（3），218-225），我们已经示出了硅和氮化硅波导之间的定量实验差异，并且演示了在SOI平台上的多位光学写入。在这个博士论文中，我们将建立在这个坚实的基础上，证明基本原理和开发（设计，制造和测试）相变为基础的计算原语，特别是突触和神经元模拟和（二进制和多级）非易失性存储器和联合收割机这样的原语到非冯诺依曼计算网络（架构）。我们将使用这些来演示与传感器处理和管理相关的国防相关计算任务的概念验证。