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.

伊辛计算是一种另一种计算范式，其灵感来自原子自旋之间的铁磁性自然物理现象。在这种非传统的计算方法中，以图形形式组织的人造自旋动态地相互作用，推动系统快速收敛到最小能量状态--最优解的表示。Ising计算机利用了这种融合行为，与经典计算机相比表现出指数级的加速，尤其擅长解决不同行业的复杂优化问题，如物流、制造、供应链管理、药物开发和金融投资组合优化。尽管正在努力使用经典和新兴技术开发Ising计算机，但都没有有效地解决与可扩展性、可重构性和连接性相关的关键挑战--这些都是实现实用Ising计算解决方案的关键因素。该项目旨在通过构建混合信号和数字专用集成电路(ASIC)硬件加速器来应对这些挑战。该项目将通过在大学引入新的本科生和研究生水平的课程来整合研究和教育，通过提供机会从事集成电路设计的实践项目，从而解决半导体行业急需的国家劳动力发展，例如，在最近的芯片和科学法案中阐明的。该项目的具体方法分为三个关键领域。首先，最初的方法旨在通过实现许多局部自旋相互作用较少的物理自旋来解决可伸缩性挑战。这包括将紧凑的锁存电路集成到混合信号伊辛计算机中，提供无需片外随机数生成器的大规模伊辛计算机，这是解决大规模组合优化问题的关键特征。除了可伸缩性，该方法还旨在通过连续时间操作利用大规模并行来大幅减少计算延迟。第二种方法将实现一台灵活的数字伊辛计算机，以解决硬件开销问题，这一问题来自于将复杂问题映射到规则网格拓扑结构(如格子图)中具有较简单互连的伊辛计算机。灵活的伊辛计算机旨在融合空间和时间(时空)自旋连接，以实现最大的可重构性，从而将硬件开销降至最低。由此产生的伊辛计算机具有灵活的自旋之间的时空交互作用，预计将显著减少所需的物理自旋数量并提高精度。最后，该项目旨在实现具有全对全自旋互连的内存中的伊辛计算机，以通过在存储器阵列中嵌入自旋来解决连接挑战，并通过大规模的交换机网络互连。这一方法旨在增强连通性和简化自旋相互作用，有助于Ising计算机的整体效率和有效性。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。