Large-scale photonic-electronic integration for next generation neuromorphic computing systems

用于下一代神经形态计算系统的大规模光子电子集成

• 批准号: 2889165
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889165
• 关键词: Large; scale; photonic; electronic; integration

Neuromorphic computing has gained huge momentum in the last decade thanks to the emergence of novel machine learning algorithms such as deep learning. Artificial neural networks are at the forefront of this revolution and their efficient hardware implementation poses significant challenges that impact on many fields of science and technology. The major problem posed by neural network computing is the handling of large matrix multiplications resulting from the parallel flux of information between densely connected layers of neurons. Photonics offers unique advantages for such demanding task as parallel operation is intrinsic to optical systems. In recent years, a number of schemes based on free-space optical setups have been proposed which have successfully implemented densely connected neural networks with hundreds of thousands of neurons. Such schemes largely rely on spatial-light modulators (SLM) to simultaneously tune millions of neuron interconnects. However, the main drawbacks of commercial SLMs are speed and integrability. An extraordinary opportunity for both high-bandwidth and integrability comes from the recent development of high-speed, high-brightness micro-light emitting diode (uLED) arrays integrated with complementary metal-oxide semiconductor (CMOS) drive electronics. In particular, gallium nitride uLED arrays with GHz-order modulation bandwidths, sub-micron pixel pitches, and large pixel counts have been demonstrated within the past few years by our group. As a result, such combined uLED-on-CMOS arrays offer integrated, reconfigurable, all-optical control eliminating the need of additional electronic tuning elements and external optical sources. In this project, the limits in terms of number of elements and bandwidth will be explored for uLED-on-COMs arrays. Moreover, this system will be combined with optical interconnectivity schemes and advanced learning algorithms to build integrated photonic neural networks. The student will gain expertise in massively parallel drive electronics (LED array drivers) and PIC design, delivering flexible photonic-electronic integration on-a-chip. Furthermore, the student will implement photonic neural network computing with immediate applicability to complex tasks like all-optical signal regeneration and processing, optical pattern recognition and smart sensing. This will require gaining expertise on free-space optical setups and advanced learning algorithms. The student will be part of a larger research group with the opportunity to work with others in a collegiate and enthusiastic team. Research findings will be published in high impact journals with the opportunity to present at international conferences.

由于新型机器学习算法（例如深度学习）的出现，在过去的十年中，神经形态计算在过去十年中取得了巨大的动力。人工神经网络处于这项革命的最前沿，其有效的硬件实施面临着对许多科学和技术领域影响的重大挑战。神经网络计算提出的主要问题是处理大型矩阵乘法，这是由于神经元密集连接层之间信息的平行通量所产生的。 Photonics为苛刻的任务提供了独特的优势，因为并行操作对于光学系统是固有的。近年来，已经提出了许多基于自由空间光学设置的方案，这些方案成功地实现了与数十万个神经元的密集连接的神经网络。这些方案在很大程度上依赖于空间光调节剂（SLM）同时调整数百万个神经元互连。但是，商业SLM的主要缺点是速度和集成性。高带宽和集成性的极大机会来自于最近与互补金属氧化物半导体（CMOS）驱动电子设备集成的高速，高光度微光发射二极管（ULED）阵列的发展。特别是，我们的小组在过去几年中证明了带有GHz阶调的带宽，亚微米像素音高和大型像素计数的氮化磁盘阵列。结果，这种组合的cmos阵列提供了集成，可重新配置的全光控制，从而消除了需要其他电子调谐元素和外部光学源的需求。在此项目中，将探索有关元素数量和带宽的限制，以探索uled-on-Coms阵列。此外，该系统将与光学互连方案和高级学习算法结合使用，以构建集成的光子神经网络。该学生将获得大量并行驱动电子（LED阵列驱动器）和PIC设计方面的专业知识，并在A-Chip上提供灵活的光子电子集成。此外，学生将立即实施光子神经网络计算计算，并在全光信号再生和处理，光学模式识别和智能传感等复杂任务中立即适用。这将需要在自由空间光学设置和高级学习算法上获得专业知识。该学生将成为一个更大的研究小组的一部分，并有机会与大学和热情团队中的其他人一起工作。研究结果将在高影响期刊上发表，并有机会参加国际会议。