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

尽管图形处理单元(GPU)等并行计算平台取得了进展，但新兴人工智能(AI)应用对高计算能力的日益增长的需求远远超过了当前电子系统的硬件效率改进。光计算有望提高效率和速度，超过传统的计算硬件，但目前受到功耗、精度和可扩展性问题的限制。该项目旨在通过解决当前光子计算架构中的这些悬而未决的问题来彻底改变人工智能领域，从而释放光子计算为人工智能带来的优势。在这份职业计划中，PI计划通过(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仿真框架来推动光学计算。除了技术进步，该项目的教育目标还包括通过负担得起的教育工具、年度STEM研讨会和指导本科生研究人员在匹兹堡培养多样化的高科技劳动力。与皮特工程教育研究中心合作的自愿评估将衡量教育成果，为长期影响提供可量化的指标。该项目旨在解决当前光子计算平台的三大局限性-耗电、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，从而改变人工智能(AI)领域。尽管并行计算取得了显著进展，但硬件效率的提高无法跟上新兴人工智能应用程序和服务对极高计算能力日益增长的需求。这主要是由于在电子领域的时钟速度和计算效率之间的基本权衡，这源于金属互连的电容和焦耳加热。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。在这份职业计划中，PI将通过三个统一的任务创造新的知识并扩展光学计算的边界：(1)集成光子学和图像传感器以实现健壮和可扩展的矩阵运算；(2)演示用于复值矩阵运算的多维光子计算；以及(3)开发一个模拟框架，以模拟所建议的用于大规模深度神经网络的硬件的计算效率和延迟。除了技术进步，PI的目标是在大匹兹堡地区培养多样化的高科技劳动力。计划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，与皮特的外联计划(LEAD)合作举办年度STEM研讨会，以及通过皮特的EXCEL暑期研究计划指导本科生研究人员。自愿评估将衡量教育成果，为项目对人工智能劳动力多样性和创新的更广泛影响提供可量化的衡量标准。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.