ASCENT: TUNA: TUnable randomness for NAtural computing

ASCENT：TUNA：TU 无法实现自然计算的随机性

• 批准号: 2230963
• 负责人: Chris Kim
• 金额: $ 150万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230963&HistoricalAwards=false
• 关键词: ASCENT; TUNA; TUnable; randomness; NAtural

A new class of computers that rely on the natural relaxation process to find the ground state, dubbed as “natural computers”, has been gaining interest. Natural computers can find the solution to hard optimization problems using room-temperature electronics, with far greater speed and efficiency than conventional digital computers. A critical challenge for natural computers is preventing the system from getting stuck in an undesirable local minima point. This necessitates intrinsic noise in the system to perturb the intermediate solutions, however, noise parameters must be carefully tuned for the system to resolve to a good ground state. This project aims at developing new tunable nanoscale magnets, modeling and benchmarking tools, and demonstrator systems for enabling practical natural computers, which is only possible through an integrated program such as ASCENT. The ability to solve hard optimization problems using natural computers will have a direct and transformative impact on our daily lives. Many industries, from manufacturing and finance to transportation and resource management, are poised to benefit from the new optimization capabilities brought about by natural computers. This inter-disciplinary project is providing growth opportunities across the entire microelectronic design stack, from device fabrication to application development.The technological advances being made by this ASCENT project is threefold. First, novel nanoscale magnetic devices with auxiliary terminals for tuning the noise characteristics are being developed. To ensure good compatibility with state-of-the-art silicon technology, the project team is investigating tuning schemes that can be integrated seamlessly into a silicon fabrication process. Second, simulation models and benchmarking tools are being developed for evaluating the tuning capabilities afforded by the new nanomagnets. Using the evaluation framework, the team is investigating various tuning modalities, such as temporal or spatial or both, to understand the impact on the computation results and energy-efficiency. Finally, several practical demonstrator systems are being built in traditional and emerging semiconductor manufacturing platforms to validate the tunable device concepts using real-world application drivers.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.

一类依靠自然弛豫过程来寻找基态的新型计算机，被称为“自然计算机”，已经引起了人们的兴趣。 自然计算机可以使用室温电子设备找到硬优化问题的解决方案，其速度和效率远高于传统数字计算机。自然计算机面临的一个关键挑战是防止系统陷入不希望的局部最小值点。这需要系统中存在固有噪声来干扰中间解，但是，必须仔细调整噪声参数以使系统解析到良好的基态。该项目旨在开发新的可调谐纳米级磁体、建模和基准测试工具以及演示系统，以实现实用的自然计算机，这只有通过 ASCENT 等集成程序才能实现。使用自然计算机解决硬优化问题的能力将对我们的日常生活产生直接和变革性的影响。 从制造和金融到运输和资源管理，许多行业都准备从自然计算机带来的新优化能力中受益。 这个跨学科项目为整个微电子设计堆栈（从器件制造到应用开发）提供了增长机会。这个 ASCENT 项目取得了三重技术进步。 首先，正在开发具有用于调节噪声特性的辅助端子的新型纳米级磁性器件。 为了确保与最先进的硅技术的良好兼容性，项目团队正在研究可以无缝集成到硅制造工艺中的调整方案。其次，正在开发模拟模型和基准测试工具，以评估新型纳米磁体提供的调谐能力。 使用评估框架，该团队正在研究各种调整方式，例如时间或空间或两者，以了解对计算结果和能源效率的影响。 最后，一些实用的演示系统正在传统和新兴的半导体制造平台中构建，以使用实际应用驱动程序验证可调谐设备概念。该奖项反映了 NSF 的法定使命，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。