Organic optoelectronic neural networks

有机光电神经网络

• 批准号: EP/Y020596/1
• 负责人: Alexander Lvovsky
• 金额: $ 73.27万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY020596%2F1
• 关键词: Organic; optoelectronic; neural; networks

In recent years, machine intelligence (MI) based on artificial neural networks has made enormous progress, entering almost all spheres of technology, economy and our everyday life. However, much of the field's current growth is reliant on an ever-increasing consumption of computational power, and as a consequence electrical power. This growing demand for larger and faster systems is unsustainable, even with the current focus on developing bespoke hardware for MI processes. Today's data centres already consume about 2% of the total power generated worldwide. This number is growing exponentially; IBM vice president of research, Mukesh Khare, extrapolated in 2019 that the power consumed by neural networks could exceed the world's electricity production by 2040. We must therefore urgently look for fundamentally new computational principles to drive MI.A promising solution to this problem is to use light, rather than electrons, as the primary carrier of information in artificial neural networks. In optical neural networks (ONNs) the wave properties of light - coherence and superposition - can streamline the "matrix multiplication" operation (the most computationally expensive operation in MI), thereby offering a new route to greatly enhance computational speeds, with dramatically lower power consumption.This project aims to advance a crucial component of the ONN: the activation function (AF). This nonlinear function is applied to each neural unit as information passes through the multiple layers of a "feedforward" neural network, serving as a "gasket" between the layers of matrix multiplications. In principle, the AF role can be played by any nonlinear optical element. In practice, however, implementation of large ONNs with purely optical AFs is challenging due to losses, lack of flexibility and error accumulation. Here we will use organic semiconductor devices to provide the activation function, with circuits of photodiodes and OLEDs transforming and transferring the signal between optical layers. This will allow us to condition the signal at each layer and correct for possible errors, while still exploiting the advantages of light propagation for the computationally expensive steps. OLED displays in smart phones can contain millions of emitters, and so the concept is potentially scalable to very large ONNs capable of performing very complex computational tasks. The project is a collaboration of two groups. The PIs at the University of St Andrews are leaders in organic semiconductor optoelectronics and the Oxford PI possesses world-leading expertise in optical computing hardware. The St Andrews group will develop the "activation chips" - integrated arrays applying the AF to multiple optical units. The Oxford group will incorporate these activation chips into ONN systems suitable for various applications. In particular, a conceptually novel ONN system for computer vision will be developed. This system will allow a neural network to "see" and interpret objects directly, bypassing the need for converting an image into an electronic form. Such a system will have ultra-low latency and could find applications in autonomous vehicles, remote sensing and intelligent robotics. We will also use the activation chips to implement the Oxford group's innovative approach to direct training of ONNs, which does not involve digital simulation and hence is both faster and more robust to errors.

近年来，基于人工神经网络的机器智能（MI）取得了巨大的进步，几乎进入了科技、经济和我们日常生活的各个领域。然而，该领域目前的大部分增长依赖于不断增长的计算能力消耗，因此也依赖于电力消耗。这种对更大更快的系统的不断增长的需求是不可持续的，即使目前的重点是为MI流程开发定制硬件。今天的数据中心已经消耗了全球总发电量的2%。这个数字呈指数级增长；IBM研究副总裁穆克什·卡雷（Mukesh Khare）在2019年推断，到2040年，神经网络消耗的电力可能会超过全球发电量。因此，我们必须迫切地寻找全新的计算原理来推动人工神经网络。解决这个问题的一个很有希望的方法是使用光，而不是电子，作为人工神经网络中信息的主要载体。在光学神经网络（ONNs）中，光的波性质——相干性和叠加性——可以简化“矩阵乘法”运算（MI中计算成本最高的运算），从而提供了一种大大提高计算速度的新途径，同时显著降低功耗。该项目旨在推进ONN的一个关键组成部分：激活功能（AF）。当信息通过“前馈”神经网络的多层时，该非线性函数应用于每个神经单元，充当矩阵乘法层之间的“垫片”。原则上，任何非线性光学元件都可以起到自动对焦的作用。然而，在实践中，由于损耗、缺乏灵活性和误差积累，使用纯光学af实现大型onn是具有挑战性的。在这里，我们将使用有机半导体器件提供激活功能，由光电二极管和oled电路在光层之间转换和传输信号。这将允许我们调整每一层的信号并纠正可能的错误，同时仍然利用光传播的优势来计算昂贵的步骤。智能手机中的OLED显示屏可以包含数百万个发射器，因此这个概念有可能扩展到能够执行非常复杂计算任务的非常大的onn。这个项目是两个小组的合作。圣安德鲁斯大学的PI是有机半导体光电子学的领导者，牛津PI在光学计算硬件方面拥有世界领先的专业知识。圣安德鲁斯团队将开发“激活芯片”——将自动对焦应用于多个光学单元的集成阵列。牛津大学的研究小组将把这些激活芯片整合到适合各种应用的ONN系统中。特别是，将开发一个概念新颖的计算机视觉ONN系统。该系统将允许神经网络直接“看到”并解释物体，而无需将图像转换为电子形式。这样的系统将具有超低延迟，可以在自动驾驶汽车、遥感和智能机器人中找到应用。我们还将使用激活芯片来实现牛津小组的创新方法来直接训练onn，该方法不涉及数字模拟，因此速度更快，抗错误能力更强。