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

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。这一观察结果激发了概率模型，这些模型对各种学习和优化任务很有用。传统的计算机不能很好地解决这类问题，因为它们基本上是确定性的。在这项工作中，研究人员建议通过在商业计算机芯片上增加热不稳定的磁性设备来开发概率单位单元，这些设备自然地显示出概率行为。这种设备的分布式网络将使仿真和加速强大的随机计算模型成为可能。大脑逆向工程是21世纪的主要挑战之一。这样的努力无疑会影响人们对计算的理解。这项建议受到神经活动的概率解释的启发，将开发一种混合概率技术作为原型，将这种洞察力有效地转化为有形的技术，然后再转化为更广泛的社区。这项研究将需要来自材料科学、设备物理、电气工程和计算机科学等不同领域的不同国际团队的贡献。这项研究的结果将以出版物、演示文稿、各种语言的YouTube教学短片和面向公众的实验室参观的形式传播。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。这一观察结果激发了概率模型，这些模型对各种学习和优化任务很有用。传统的计算机不能很好地解决这类问题，因为它们基本上是确定性的。在这项工作中，研究人员建议通过在商业计算机芯片上增加热不稳定的磁性设备来开发概率单位单元，这些设备自然地显示出概率行为。这种设备的分布式网络将使仿真和加速强大的随机计算模型成为可能。大脑逆向工程是21世纪的主要挑战之一。这样的努力无疑会影响人们对计算的理解。这项建议受到神经活动的概率解释的启发，将开发一种混合概率技术作为原型，将这种洞察力有效地转化为有形的技术，然后再转化为更广泛的社区。这项研究将需要来自材料科学、设备物理、电气工程和计算机科学等不同领域的不同国际团队的贡献。这项研究的结果将以出版物、演示文稿、各种语言的YouTube教学短片和面向公众的实验室参观的形式传播。这一奖项反映了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。

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.