CAREER: Scalable Physics-Inspired Ising Computing for Combinatorial Optimizations

职业：用于组合优化的可扩展物理启发伊辛计算

• 批准号: 2340453
• 负责人: Bongjin Kim
• 金额: $ 58.91万
• 依托单位: University of California-Santa Barbara
• 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: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340453&HistoricalAwards=false
• 关键词: CAREER; Scalable; Physics; Inspired; Ising

Ising computing is an alternative computing paradigm inspired by the natural physical phenomena of ferromagnetism among atomic spins. In this unconventional computing approach, artificial spins organized in a graph dynamically interact, propelling the system toward rapid convergence to the minimum energy state – a representation of the optimal solution. The Ising computer, leveraging such convergence behavior, exhibits exponential acceleration compared to classical counterparts, particularly excelling in solving intricate optimization problems across diverse sectors such as logistics, manufacturing, supply chain management, drug discovery, and financial portfolio optimization. Despite ongoing efforts to develop Ising computers using classical and emerging technologies, none have effectively addressed critical challenges related to scalability, reconfigurability, and connectivity – essential factors for realizing practical Ising computing solutions. This project aims to tackle these challenges by constructing mixed-signal and digital application-specific integrated circuit (ASIC) hardware accelerators. The project will integrate research and education by introducing new undergraduate and graduate level courses at the university, by providing opportunity to work on hands-on projects on integrated circuit design, thus addressing a much-needed national workforce development for the Semiconductor Industry as, e.g., articulated in the recent Chips and Science Act.The specific approaches of the project are categorized into three key areas. Firstly, the initial approach aims to tackle scalability challenges by implementing many physical spins with fewer local spin interactions. This involves integrating compact latch circuits in a mixed-signal Ising computer, providing a large-scale Ising computer without the need for off-chip random number generators, which is a crucial feature for addressing large-scale combinatorial optimization problems. Beyond the scalability, the approach also aims to substantially reduce computing latency by leveraging massive parallelism through continuous-time operation. The second approach will implement a flexible digital Ising computer to address the issue of hardware overhead, which comes from mapping complex problems to the Ising computer with simpler interconnects in a regular grid topology, such as a lattice graph. The flexible Ising computer aims to amalgamate spatial and temporal (spatio-temporal) spin connectivity to achieve maximum reconfigurability, thereby minimizing hardware overhead. The resulting Ising computer with flexible spatio-temporal interactions between spins is anticipated to significantly reduce the required number of physical spins and enhance accuracy. Lastly, this project aims to implement an in-memory Ising computer with all-to-all spin interconnects to address connectivity challenges by embedding spins in the memory array, interconnected via a massive network of switches. This approach is designed to enhance connectivity and streamline spin interactions, contributing to the overall efficiency and effectiveness of the Ising computer.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.

Ising计算是灵感来自原子旋转中铁磁性自然物理现象的替代计算范式。在这种非常规计算方法中，在图形中组织的人造旋转动态相互作用，将系统促进快速收敛到最低能量状态 - 最佳解决方案的表示。利用这种融合行为的Ising计算机与经典的计算机相比表现出指数加速度，在解决潜水员，制造，供应链管理，药物发现和金融投资组合优化等领域的复杂优化问题方面尤其出色。尽管持续不断地使用经典技术和新兴技术开发计算机，但没有一个有效地解决了与可伸缩性，可重构性和连接性相关的关键挑战 - 实现实用的ISSING计算解决方案的基本因素。该项目旨在通过构建混合信号和数字应用特定的集成电路（ASIC）硬件加速器来应对这些挑战。该项目将通过在大学中引入新的本科和研究生水平课程来整合研究和教育，从而提供机会进行综合巡回赛设计的动手项目，从而解决急需的半导体行业国家劳动力发展，例如，在最近的CHIPS和Science Act中阐明了该项目。首先，最初的方法旨在通过实施许多本地旋转相互作用的旋转来应对可伸缩性挑战。这涉及在混合信号计算机中集成紧凑型闩锁电路，提供大规模的ISING计算机而无需外芯片随机数生成器，这是解决大规模组合优化问题的关键功能。除了可伸缩性之外，该方法还旨在通过连续时间操作利用大量并行性来大大减少计算潜伏期。第二种方法将实施灵活的数字计算机来解决硬件开销问题，这是从将复杂问题映射到伊辛计算机上，并在常规网格拓扑中使用更简单的互连，例如晶格图。灵活的Ising计算机旨在将空间和临时（时空）旋转连接融合以实现最大的重新配置，从而最大程度地减少了硬件开销。预计旋转之间具有柔性时空相互作用的由此产生的计算机预计将显着降低所需的物理旋转数量并提高精度。最后，该项目旨在实现具有全面旋转互连的内存计算机，以通过嵌入内存阵列中的旋转来解决连接挑战，并通过大量的交换机网络互连。这种方法旨在增强连通性和简化旋转相互作用，从而有助于伊辛计算机的整体有效性和有效性。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估标准来评估，被认为是珍贵的支持。