CAREER: Multi-Dimensional Photonic Accelerators for Scalable and Efficient Computing

职业：用于可扩展和高效计算的多维光子加速器

• 批准号: 2337674
• 负责人: Nathan Youngblood
• 金额: $ 55.22万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337674&HistoricalAwards=false
• 关键词: CAREER; Multi; Dimensional; Photonic; Accelerators

Despite advances in parallel computing platforms such as graphics processing units (GPUs), the growing demand for high computing power in emerging artificial intelligence (AI) applications far exceeds hardware efficiency improvements in current electronic systems. Optical computing promises improved efficiency and speed over conventional computing hardware, but is currently limited by power consumption, precision, and scalability issues. This project aims to revolutionize the field of AI by addressing these outstanding issues in current photonic computing architectures, thus unleashing the advantages of photonic computing for AI. In this CAREER proposal, the PI plans to advance optical computing through: (1) integrating photonic circuits and image sensors, (2) demonstrating multi-dimensional photonic computing, and (3) developing a simulation framework for large-scale photonic neural networks. Beyond technical advancements, the project’s educational goals include cultivating a diverse high-tech workforce in Pittsburgh through affordable educational tools, annual STEM workshops, and mentoring undergraduate researchers. Voluntary assessments in collaboration with Pitt's Engineering Education Research Center will measure educational outcomes, providing quantifiable metrics for long-term impact.This project aims to address three major limitations of current photonic computing platforms—power hungry electrical readout, limited analog precision, and poor scalability—to enable a fast and efficient computing architecture which could transform the field of artificial intelligence (AI). Despite notable advances in parallel computing, gains in hardware efficiency are unable to keep pace with the growing demand for extremely high computing power required by emerging AI applications and services. This is primarily due to the fundamental trade-off between clock speed and computational efficiency in the electronic domain stemming from the capacitance and Joule heating of metal interconnects. The PI will address this fundamental issue by performing computation in the optical domain using multiple photonic degrees of freedom for improved compute efficiency and speed. In this CAREER proposal, the PI will create new knowledge and extend the boundaries of optical computing through three unified tasks which: (1) integrate photonics and image sensors for robust and scalable matrix operations; (2) demonstrate multi-dimensional photonic computing for complex-valued matrix operations; and (3) develop a simulation framework to model the compute efficiency and latency of the proposed hardware for large-scale deep neural networks. Beyond technical advancements, the PI aims to cultivate a diverse high-tech workforce in the greater Pittsburgh area. Initiatives include creating affordable educational tools exposing students to nanotechnology applications in AI, conducting annual STEM workshops in collaboration with Pitt's outreach program (LEAD), and mentoring undergraduate researchers through Pitt's EXCEL summer research program. Voluntary assessments will measure educational outcomes, providing quantifiable metrics for the project's broader impact on workforce diversity and innovation in AI.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 criteria.

尽管图形处理单元（GPU）等并行计算平台取得了进步，但新兴人工智能（AI）应用对高计算能力不断增长的需求远远超过了当前电子系统中硬件效率的提高，光学计算有望比传统计算提高效率和速度。该项目旨在通过解决当前光子计算架构中的这些突出问题来彻底改变人工智能领域，从而释放光子计算在人工智能领域的优势。 ，P.I.计划通过以下方式推进光学计算：(1) 集成光子电路和图像传感器，(2) 演示多维光子计算，以及 (3) 开发大规模光子神经网络的模拟框架 除了技术进步之外，该项目的教育意义。目标包括通过负担得起的教育工具、年度 STEM 研讨会以及指导本科生研究人员，在匹兹堡培养多元化的高科技劳动力。与匹兹堡工程教育研究中心合作的自愿评估将衡量教育成果，提供长期影响的量化指标。该项目旨在解决当前光子计算平台的三个显着限制——耗电的电读出、有限的模拟精度和较差的可扩展性——以实现快速高效的计算架构，尽管并行取得了进步，但该架构可以改变人工智能（AI）领域。计算方面，硬件效率的提高无法跟上新兴人工智能应用和服务对极高计算能力不断增长的需求，这主要是由于电子领域的时钟速度和计算效率之间的根本权衡。金属的电容和焦耳热PI 将通过使用多个光子自由度在光域中执行计算来解决这一基本问题，以提高计算效率和速度。在这个职业提案中，PI 将通过三个统一来创造新知识并扩展光计算的边界。任务：(1) 集成光子学和图像传感器以实现稳健且可扩展的矩阵运算；(2) 演示用于复值矩阵运算的多维光子计算；(3) 开发一个模拟框架来模拟计算效率和延迟建议的硬件除了技术进步之外，PI 还致力于在大匹兹堡地区培养多元化的高科技劳动力，其举措包括创建负担得起的教育工具，让学生接触人工智能中的纳米技术应用，与匹兹堡大学合作举办年度 STEM 研讨会。外展计划（LEAD），并通过皮特的 EXCEL 夏季研究计划指导本科生研究人员将衡量教育成果，为该项目对劳动力多样性和人工智能创新的更广泛影响提供量化指标。该奖项反映了 NSF 的法定奖项。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。