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，几乎进入了技术，经济和我们的日常生活领域。但是，该领域的大部分当前增长都依赖于不断增加的计算能力消耗，也是电力的。即使目前专注于为MI流程开发定制硬件，对更大，更快的系统的需求日益增长。当今的数据中心已经消耗了全球产生的总功率的2％。这个数字呈指数增长； IBM研究副总裁Mukesh Khare于2019年推断出，神经网络所消耗的功率可能会在2040年到2040年超过世界的电力生产。因此，我们必须急切地寻找从根本上寻找新的计算原理来推动MI. MI.MI.一种有希望的解决方案，以解决此问题的解决方案，而不是使用电子，而不是电子网络，作为人工神经网络中的主要信息。 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).随着信息通过“进发液”神经网络的多层，将此非线性函数应用于每个神经单元，并用作矩阵乘法层之间的“垫圈”。原则上，任何非线性光学元件都可以扮演AF的角色。但是，实际上，由于损失，缺乏灵活性和误差积累，具有纯粹光学AFS的大型ONN的实施是具有挑战性的。在这里，我们将使用有机半导体设备来提供激活函数，光电二极管和OLED的电路会在光学层之间转换和传递信号。这将使我们能够在每一层处调节信号并纠正可能的错误，同时仍利用光传播的优势来计算昂贵的步骤。智能手机中的OLED显示器可能包含数百万发射器，因此该概念可能可扩展到能够执行非常复杂的计算任务的非常大的ONN。该项目是两组的合作。圣安德鲁斯大学的PI是有机半导体光电学领导者，牛津Pi在光学计算硬件方面具有世界领先的专业知识。 St Andrews组将开发“激活芯片” - 集成阵列，将AF应用于多个光学单元。牛津集团将将这些激活芯片纳入适用于各种应用的ONN系统。特别是，将开发一个概念上新颖的计算机视觉ONN系统。该系统将允许神经网络“看到”并直接解释对象，从而绕开将图像转换为电子形式的需求。这样的系统将具有超低潜伏期，并可以在自动驾驶汽车，遥感和智能机器人技术中找到应用。我们还将使用激活芯片来实施牛津集团的创新方法来直接培训ONNS，这不涉及数字模拟，因此对错误既更快又更强大。