SBIR Phase I: Energy Efficient Superconducting Neuromorphic Computing Circuits

SBIR 第一阶段：节能超导神经形态计算电路

• 批准号: 2136676
• 负责人: Ryan Goul
• 金额: $ 25.6万
• 依托单位: ZENOLEAP LLC
• 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: 2022-08-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136676&HistoricalAwards=false
• 关键词: SBIR; Phase; Energy; Efficient; Superconducting

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is potential commercial development of superconducting neuromorphic computing (NC) circuits with the ability to enable true biological brain-inspired deep neural network circuit algorithms and to improve efficiency, speed, and scalability of NC by orders of magnitude. The knowledge and approaches developed through this effort may help advance the foundational development of next generation computing hardware, helping NC continue its advance toward broad market adoption, and helping the US maintain its position as a leader in processor development and production. Additionally, the integrated synthesis-characterization-application approach can be extended to a range of applications, including sensors, metamaterials, catalysis, and renewables, which require atomic-scale control of materials and interfaces. Finally, through a partnership with University of Kansas, the project will facilitate university technology transfer and will serve to educate the next generation of materials and advanced electronics scientists and engineers. The atomic-to-nanoscale design, fabrication, characterization, and application experience will not only assist in recruiting top-quality students and provide them opportunities for entrepreneurship.This Small Business Innovation Research (SBIR) Phase I project seeks to develop novel superconducting neuromorphic computing (NC) circuits consisting of atomically tunable memristors (synapses) with superconductor interconnects and superconducting quantum interference devices (SQUIDs, neurons). This superconducting NC circuit aims to enable true biological brain-inspired deep network circuit algorithms and to achieve currently unattainable levels of energy efficiency, switching speed, and scalability in NC. The proposed research will design, fabricate, and characterize superconducting memristor-SQUID NC circuit hardware including development of the corresponding algorithms for pattern recognition, with machine learning capabilities, using the Modified National Institute of Standards and Technology database to prove viability. The intellectual merit of the proposed research is illustrated in: (1) novel, atomically-tunable memristors with 3-4 orders of magnitude dynamic range in the on/off ratio and switching frequency that can enable spikes of different amplitudes and frequencies as demanded for emerging deep circuits, (2) SQUID neurons with very high sensitivity and low noise, and (3) neurons and interconnects that can significantly reduce power consumption by eliminating the parasitic wire resistance that, in current NC circuits, increases substantially with circuit scale.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响是超导神经形态计算（NC）电路的潜在商业开发，能够实现真正的生物大脑启发的深度神经网络电路算法，并将NC的效率，速度和可扩展性提高几个数量级。通过这一努力开发的知识和方法可能有助于推进下一代计算硬件的基础开发，帮助NC继续向广泛的市场采用迈进，并帮助美国保持其作为处理器开发和生产领导者的地位。此外，集成的合成-表征-应用方法可以扩展到一系列应用，包括传感器，超材料，催化和可再生能源，这些应用需要对材料和界面进行原子级控制。最后，通过与堪萨斯大学的合作，该项目将促进大学技术转让，并将有助于教育下一代材料和先进电子科学家和工程师。这项小型企业创新研究（SBIR）第一期项目旨在开发由原子可调忆阻器组成的新型超导神经形态计算（NC）电路（突触）与超导体互连和超导量子干涉装置（SQUID，神经元）。这种超导NC电路旨在实现真正的生物大脑启发的深度网络电路算法，并在NC中实现目前难以达到的能源效率、开关速度和可扩展性水平。 拟议的研究将设计，制造和表征超导忆阻器-SQUID NC电路硬件，包括开发相应的模式识别算法，具有机器学习能力，使用修改后的美国国家标准与技术研究所数据库来证明可行性。拟议研究的智力价值说明：（1）在开/关比和开关频率上具有3-4个数量级动态范围的新型原子可调忆阻器，其可以实现新兴深度电路所需的不同幅度和频率的尖峰，（2）具有非常高灵敏度和低噪声的SQUID神经元，以及（3）通过消除寄生导线电阻可以显著降低功耗的神经元和互连，在当前的NC电路中，该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。