FuSe: Co-designed Systems for In-sensor Processing with Sustainable Nanomaterials (COSMIC)

FuSe：利用可持续纳米材料共同设计的传感器内处理系统 (COSMIC)

• 批准号: 2328712
• 负责人: Tania Roy
• 金额: $ 137.62万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328712&HistoricalAwards=false
• 关键词: FuSe; Co; designed; Systems; sensor

The proposal aims at developing a new technology for artificial intelligence (AI) and machine learning (ML). Using environmentally sustainable materials, a system for processing information – specifically for image processing – will be developed. The current AI models, such as OpenAI’s ChatGPT, require a massive amount of power and resources to run. More companies are rushing to create their ChatGPT clones, which is not sustainable and will only exacerbate climate change. Specialized hardware for vision sensing that is smaller in footprint and more energy-efficient to run AI models on conventional devices like laptops, without relying on cloud servers, is proposed. Artificial vision sensors can transform the future by enabling vision beyond the human eye’s capabilities, leading to impact across the entire landscape of modern life. By detecting changes in motion, it will lead to a new generation of self-driving cars for future “smarter” cities. The proposed vision sensors will enable improved monitoring of processes in the industry, and improved understanding of body movements of athletes, all with low energy consumption and on a small area. Future products enabled by the scientific discoveries and advancements of this work will augment the semiconductor industry. For instance, improved synthesis processes for electronic nanomaterials that are fully recyclable will supplement the industry’s growth, creating new job opportunities. Semiconductor workforce development will be strongly emphasized and enabled by this grant, and students trained through this project will be equipped to join the multitudes of industrial efforts accompanied by the CHIPS for America act and other initiatives. Local college students will be exposed to the developments and trained as a workforce for the semiconductor industry. High school teachers will be exposed to cutting-edge research in semiconductors to convey the enthusiasm of the field to their students.A materials-devices-system co-design for in-sensor computing with pixel arrays heterogeneously integrated with crossbar arrays to form a convolutional neural network (CNN) for image processing, with a special emphasis on sustainable manufacturing, is proposed by the Co-designed Systems for In-sensor Processing with Sustainable Nanomaterials (COSMIC) team. The team will pursue co-design and heterogeneous integration for bio-inspired sensing-to-action to optimize power consumption, performance, and hardware footprint using machine learning architectures. Pursuit of these goals will be undertaken by experts in neuromorphic devices (Roy), nanomaterials for sustainable electronics (Franklin), and neuromorphic circuits and algorithms (Li). The objectives are to: A. Develop in-pixel computing circuits with the highest possible fill factor of each pixel; B. Integrate the pixel arrays with a crossbar-based in-memory computing circuitry to realize a CNN for image classification and segmentation; and C. Develop the entire process with recyclable materials to reduce electronic waste. For in-pixel computing, novel optoelectronic synaptic devices using two-dimensional materials as channel and gate electrode, with multi-wavelength sensing capabilities will be designed and optimized. The in-pixel computing circuit forms the first layer of the CNN. Subsequent layers of the CNN will be realized with crossbar arrays of memristive synapses. Materials-device co-design will ensure the best characteristics of the synaptic devices for in-pixel computing and crossbar implementation, while device-system co-design will mitigate the remaining non-idealities while maximizing performance through a hybrid analog/digital computing scheme. Peripheral circuits with nanomaterials will be co-designed based on device performance and precision requirements of the crossbar circuits. The recyclability of all circuits will be studied and ensured for sustainable manufacturing. Finally, the optoelectronic synapse-based pixel arrays will be heterogeneously integrated with the crossbar arrays and peripheral circuitry, and the performance of this hardware for image processing tasks, such as classification and segmentation, will be evaluated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteriaThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该提案旨在开发一种新技术的人工智能（AI）和机器学习（ML）。使用环境可持续的材料，将开发用于处理信息的系统 - 专门用于图像处理。当前的AI模型，例如OpenAI的Chatgpt，需要大量的功率和资源来运行。越来越多的公司急于创建其Chatgpt克隆，这是不可持续的，只会加剧气候变化。提出了专门的硬件，用于占用足迹较小的视觉传感器，并且提出了在不依赖云服务器（例如笔记本电脑）上运行AI模型的更节能的效率。人造视觉传感器可以通过使视力超出人眼的能力来改变未来，从而在现代生活的整个景观中产生影响。通过检测运动的变化，它将导致新一代的自动驾驶汽车，以实现未来的“智能”城市。拟议的视力传感器将能够改善对行业过程的监测，并改善对运动员的身体运动的理解，所有这些都具有低能消耗和小区域。这项工作的科学发现和进步可以增强半导体行业的未来产品。例如，改进了完全可回收的电子纳米材料的合成过程，将补充该行业的增长，从而创造新的就业机会。该赠款将强烈强调和启用半导体劳动力发展，并且通过该项目培训的学生将有能力加入《美国筹码法案》和其他计划所实现的众多工业努力。当地的大学生将接触到这些发展，并作为半导体行业的劳动力进行培训。高中教师将接触半导体的尖端研究，以传达该领域的热情。使用可持续纳米材料（宇宙）团队进行处理。该团队将通过使用机器学习体系结构来优化功能消耗，性能和硬件足迹，以进行生物启发的感官对动作进行共同设计和异质集成。对这些目标的追求将由神经形态设备（ROY）的专家，可持续电子产品的纳米材料（富兰克林）以及神经形态电路和算法（LI）实现。对象是：A。开发每个像素的最高填充因子的像素内计算电路； B.将像素阵列与基于横杆的内存计算电路集成，以实现用于图像分类和分割的CNN；和C.使用可回收材料来开发整个过程以减少电子废物。对于像素内计算，将设计和优化具有二维材料（作为通道和栅极电子）的新型光电突触设备，具有多波长感测功能。像素内计算电路形成了CNN的第一层。随后的CNN层将实现。带有回忆突触的横杆阵列。材料设备的共同设计将确保突触设备的最佳特征用于像素计算和横梁实现，而设备系统共同设计将减轻其余的非理想性，同时通过混合模拟/数字计算方案最大程度地提高性能。带有纳米材料的外围电路将根据设备性能和横杆电路的精确要求共同设计。将研究和确保所有电路的可回收性用于可持续制造。最后，将评估基于光电子突触的像素阵列与横梁阵列和外围电路的异质整合，并且将评估该硬件用于图像处理任务的硬件（例如分类和细分）的性能。该奖项通过NSF的法定任务和评估的奖励，已被视为宣布的奖项，并已被视为众所周知的范围。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的审查标准来评估，被认为是宝贵的支持。