ASCENT: TUNA: TUnable randomness for NAtural computing
ASCENT: TUNA: TUnable randomness for NAtural computing
批准号:
2230963
负责人:
Chris Kim
金额:
$150.0万
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
项目状态:
未结题
起止时间:
2022-10-01 至 2026-09-30
中文摘要
一类新的计算机,依靠自然松弛过程来找到基态,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学找到困难优化问题的解决方案,其速度和效率远远高于传统的数字计算机。自然计算机面临的一个关键挑战是防止系统陷入不期望的局部极小点。这需要系统中的固有噪声来扰动中间解,然而,噪声参数必须被仔细地调谐以使系统解析到良好的基态。该项目旨在开发新的可调纳米级磁体,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等集成计划才能实现。使用自然计算机解决困难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。 从制造业、金融业到运输业和资源管理业,许多行业都将受益于自然计算机带来的新的优化能力。 这个跨学科的项目为整个微电子设计堆栈提供了增长机会,从器件制造到应用开发。这个ASCENT项目所取得的技术进步是三方面的。 首先,正在开发具有用于调谐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谐方案。其次,正在开发模拟模型和基准测试工具,以评估新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优模式,例如时间或空间或两者兼而有之,以了解对计算结果和能源效率的影响。 最后,在传统和新兴的半导体制造平台上正在建造几个实用的演示系统,以验证使用真实世界应用驱动程序的可调器件概念。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
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.
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