L2S for CMOS Image Sensor Design and Bridging the Gap

用于 CMOS 图像传感器设计并缩小差距的 L2S

• 批准号: 498557035
• 负责人: Professor Dr. Bhaskar Choubey
• 金额: --
• 依托单位: Lehrstuhl Analoge Schaltungstechnik und Bildgebende Sensorsysteme
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498557035?language=en
• 关键词: L2S; CMOS; Image; Sensor; Design

CMOS image sensors have been key to revolutionise our life recently. In addition, images produced by these digital cameras are also a leading contributor to recent growth in AI technologies. However, traditional digital cameras have been built to match displays like computer monitors and paper, rather than being optimised for various machine learning tasks. Despite a number of AI algorithms being inspired by the animal brain, the image sensor seeks little inspiration from animal eye and undertakes almost no image processing. As a result, most digital cameras are mere data producing inputs with limited information extraction. In this project, we will develop and build novel image sensor architectures which are co-designed with intelligent algorithms. To do so, at one hand we will revisit the pixel geometry and layout, redesigning to provide optimal inputs to several image processing tasks developed by our collaborators in other L2S projects. Simultaneously, we will embed early analogue signal processing close to the pixel and the array in form of mathematical operators like derivatives and convolution as well as input stages of typical neural networks. These will be designed to reduce the complexity of the succeeding computational tasks of AI systems in software. The collaborative research between sensor developer and AI researchers will ensure that neither optical nor computation efficiency is lost in this process. Furthermore, this project will build bridge between intelligent algorithm researchers and several sensor developers by coordinating the efforts of knowledge exchange and joint research between the two communities.

最近，CMOS图像传感器已经成为改变我们生活的关键。此外，这些数码相机产生的图像也是人工智能技术近期增长的主要贡献者。然而，传统的数码相机是为了匹配计算机显示器和纸张等显示器而设计的，而不是针对各种机器学习任务进行优化。尽管许多人工智能算法受到动物大脑的启发，但图像传感器很少从动物眼睛中寻求灵感，几乎不进行图像处理。因此，大多数数码相机仅仅是数据产生输入，信息提取有限。在这个项目中，我们将开发和构建与智能算法共同设计的新型图像传感器架构。为此，一方面，我们将重新审视像素的几何形状和布局，重新设计，为我们的合作者在其他L2S项目中开发的几个图像处理任务提供最佳输入。同时，我们将以数学运算符（如导数和卷积）以及典型神经网络的输入级的形式，在像素和阵列附近嵌入早期模拟信号处理。这些将被设计为降低软件中AI系统后续计算任务的复杂性。传感器开发人员和人工智能研究人员之间的合作研究将确保在此过程中不会损失光学或计算效率。此外，该项目将通过协调两个社区之间的知识交流和联合研究，在智能算法研究人员和几个传感器开发人员之间建立桥梁。