STTR Phase I: Room Temperature Implementation of Quantum Algorithms using Spintronics Technology

STTR 第一阶段：使用自旋电子技术在室温下实现量子算法

• 批准号: 2035962
• 负责人: Behtash Behin-Aein
• 金额: $ 25.6万
• 依托单位: QCML LABS, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035962&HistoricalAwards=false
• 关键词: STTR; Phase; Room; Temperature; Implementation

The broader impact of this Small Business Technology Transfer (STTR) Phase I project will be to enable cheaper, faster, and more energy-efficient semiconductor chips dedicated to solving challenges with many possible outcomes and inherent uncertainty. Such problems are encountered in decision-making, risk management, chip design, drug design, business analytics, machine learning, computing failure rates of manufactured products, pricing complex financial derivatives, resource allocation in 5G networks and financial portfolios. Current methods for addressing these problems are intensive, time-consuming, and expensive. The proposed chips would quantify the uncertainty and find suitable solutions to many real-world challenges with faster, cheaper, and more energy-efficient methods.This Small Business Technology Transfer (STTR) Phase I project focuses on advancing probabilistic computers for probabilistic computing. Decision-making, econometrics, risk management, chip design, and drug development are not deterministic. By leveraging the natural stochasticity of specific devices, it is possible to dramatically reduce the number of devices otherwise used for the same computational tasks. The proposed computational architecture and pipelining enables improved computational speed, power consumption, and chip manufacturing costs. The goals of this project are two-fold. The first is to deliver 10-1000x improvement in computation speed compared to CPUs and GPUs using the emulation of probabilistic computers on Field Programmable Gate Arrays. The same task will make projections about the performance of the actual probabilistic computer. The second is to quantify how well the probabilistic computer can accommodate semiconductor manufacturing process variations. Controlling such variations is a significant factor in semiconductor manufacturing costs.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.

这一小型企业技术转让(STTR)第一阶段项目的更广泛影响将是实现更便宜、更快和更节能的半导体芯片，专门用于解决存在许多可能结果和内在不确定性的挑战。这些问题在决策、风险管理、芯片设计、药物设计、商业分析、机器学习、制造产品失败率计算、复杂金融衍生品定价、5G网络资源配置和金融投资组合中都会遇到。目前解决这些问题的方法密集、耗时且昂贵。建议的芯片将量化不确定性，并以更快、更便宜和更节能的方法找到适合许多现实世界挑战的解决方案。这个小型企业技术转移(STTR)第一阶段项目专注于推动概率计算的概率计算机。决策、计量经济学、风险管理、芯片设计和药物开发都不是确定性的。通过利用特定设备的自然随机性，可以显著减少用于相同计算任务的设备数量。建议的计算架构和流水线可以提高计算速度、功耗和芯片制造成本。这个项目的目标有两个。首先是使用现场可编程门阵列上的概率计算机仿真，使计算速度比CPU和GPU提高10-1000倍。同样的任务将对实际概率计算机的性能进行预测。第二个是量化概率计算机适应半导体制造工艺变化的能力。控制此类变化是半导体制造成本中的一个重要因素。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。