CISE-ANR: FET: Small: Hybrid Stochastic Tunnel Junction Circuits for Optimization and Inference

CISE-ANR：FET：小型：用于优化和推理的混合随机隧道结电路

• 批准号: 2121957
• 负责人: Advait Madhavan
• 金额: $ 49.73万
• 依托单位: University of Maryland, College Park
• 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: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121957&HistoricalAwards=false
• 关键词: CISE; ANR; FET; Small; Hybrid

Neuroscience research shows that pervasive randomness in brains is fundamental to their stability and computational ability. This observation inspires probabilistic models that are useful for a variety of learning and optimization tasks. Conventional computers are not well suited to solving such problems because they are fundamentally deterministic. In this work, the researchers propose to develop probabilistic unit cells by augmenting commercial computer chips with thermally unstable magnetic devices that naturally exhibit probabilistic behavior. Distributed networks of such devices will enable emulating and accelerating powerful stochastic computational models. Reverse engineering the brain is one of the major challenges of the 21st century. Such an endeavor will undoubtedly affect the way computation is understood. This proposal, inspired by a probabilistic interpretation of neural activity will develop a hybrid probabilistic technology as a prototype to efficiently transfer this insight into a tangible technology and then to the broader community. The research will require contributions of a diverse international team from a variety of fields such as material science, device physics, electrical engineering, and computer science. The results of this research will be disseminated in the form of publications, presentations, short pedagogical YouTube videos in various languages, and lab tours for the general public.Neuroscience research shows that pervasive randomness in brains is fundamental to their stability and computational ability. This observation inspires probabilistic models that are useful for a variety of learning and optimization tasks. Conventional computers are not well suited to solving such problems because they are fundamentally deterministic. In this work, the researchers propose to develop probabilistic unit cells by augmenting commercial computer chips with thermally unstable magnetic devices that naturally exhibit probabilistic behavior. Distributed networks of such devices will enable emulating and accelerating powerful stochastic computational models. Reverse engineering the brain is one of the major challenges of the 21st century. Such an endeavor will undoubtedly affect the way computation is understood. This proposal, inspired by a probabilistic interpretation of neural activity will develop a hybrid probabilistic technology as a prototype to efficiently transfer this insight into a tangible technology and then to the broader community. The research will require contributions of a diverse international team from a variety of fields such as material science, device physics, electrical engineering, and computer science. The results of this research will be disseminated in the form of publications, presentations, short pedagogical YouTube videos in various languages, and lab tours for the general public.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.

神经科学研究表明，大脑中普遍存在的随机性是其稳定性和计算能力的基础。这一观察启发了概率模型，这些模型对各种学习和优化任务都很有用。传统的计算机不太适合解决这类问题，因为它们基本上是确定性的。在这项工作中，研究人员建议通过用自然表现出概率行为的热不稳定磁性器件增强商用计算机芯片来开发概率单位单元。这种设备的分布式网络将能够模拟和加速强大的随机计算模型。 对大脑进行逆向工程是21世纪世纪的主要挑战之一。这样的奋进无疑会影响人们对计算的理解。 这项提案的灵感来自于对神经活动的概率解释，它将开发一种混合概率技术作为原型，以有效地将这种见解转化为有形技术，然后推广到更广泛的社区。这项研究将需要来自材料科学、器件物理、电气工程和计算机科学等各个领域的多元化国际团队的贡献。这项研究的成果将以出版物、演讲、各种语言的YouTube教学短片以及面向公众的实验室图尔斯参观等形式传播。神经科学研究表明，大脑中普遍存在的随机性是其稳定性和计算能力的基础。这一观察启发了概率模型，这些模型对各种学习和优化任务都很有用。传统的计算机不太适合解决这类问题，因为它们基本上是确定性的。在这项工作中，研究人员建议通过用自然表现出概率行为的热不稳定磁性器件增强商用计算机芯片来开发概率单位单元。这种设备的分布式网络将能够模拟和加速强大的随机计算模型。 对大脑进行逆向工程是21世纪世纪的主要挑战之一。这样的奋进无疑会影响人们对计算的理解。 这项提案的灵感来自于对神经活动的概率解释，它将开发一种混合概率技术作为原型，以有效地将这种见解转化为有形技术，然后推广到更广泛的社区。这项研究将需要来自材料科学、器件物理、电气工程和计算机科学等各个领域的多元化国际团队的贡献。该研究成果将以出版物、演讲、各种语言的YouTube教学短片以及面向公众的实验室图尔斯之旅的形式传播。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。