E2CDA: Type I: Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing

E2CDA：类型 I：用于低能量布尔和非布尔计算的概率自旋逻辑

• 批准号: 1739635
• 负责人: Joerg Appenzeller
• 金额: $ 245.38万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739635&HistoricalAwards=false
• 关键词: E2CDA; Type; Probabilistic; Spin; Logic

Various components of hardware and software have come together to make this electronic revolution surrounding smartphones and the like possible. To further expand our capabilities, to enter a new era of even more comprehensive networking between individual users and the cloud, as well as to solve problems of a complexity level that cannot be tackled even with today's computers, there is the need to push the frontiers of data processing beyond anything that can be achieved through "conventional" electronics. One approach is to improve the hardware further. However the latter has been historically mainly focused on making devices ever smaller, thus gaining the benefit of a more compact but very power hungry electronics. The current project revolves around a novel device that uses electron spins rather than electron charges for information processing, and holds the promise to achieve the above mentioned goals at a power consumption level that is far smaller than what can be envisioned with conventional technologies. At the same time the project aims at a new level of education and dissemination through the concept that we call "atoms to systems". A new generation of engineers requires skill sets and a knowledge base that is different from what was learned previously. A high priority of the project is thus to make the models and ideas that underlie them accessible across science and engineering.There is increasing interest in a fundamentally different form of brain-like logic based on probabilistic inference that is far more effective and energy efficient in dealing with the problems of search and recognition posed by the ever increasing amounts of "big data". Probabilistic logic is currently implemented with software algorithms that run on a deterministic computing platform. The goal is to lay the foundation for a new P(robabilistic)-computing platform using unstable multiferroics, a manmade material combination that combines distinct electrical, mechanical and magnetic properties. Unlike quantum computers, P-computers should operate robustly at room temperature, while providing some aspects of the "quantum parallelism" that facilitates the solution of hard problems. The team will work on the material, device and circuit development of this novel probabilistic computing idea. Technical work related to the characterization of novel material properties, the impact of various structural parameters and architectural aspects will all be explored in parallel by the group of experts.

硬件和软件的各种组件已经走到一起，使围绕智能手机等的电子革命成为可能。为了进一步扩展我们的能力，进入个人用户和云之间更全面的网络的新时代，以及解决即使用今天的计算机也无法解决的复杂问题，有必要推动数据处理的前沿超越任何可以通过“传统”电子产品实现的东西。一种方法是进一步改进硬件。然而，后者在历史上主要专注于制造更小的设备，从而获得更紧凑但非常耗电的电子产品的好处。目前的项目围绕着一种新的设备，该设备使用电子自旋而不是电子电荷进行信息处理，并有望以远小于传统技术所能想象的功耗水平实现上述目标。与此同时，该项目旨在通过我们称之为“原子到系统”的概念，将教育和传播提高到一个新的水平。新一代工程师需要的技能和知识基础与以前学到的不同。因此，该项目的一个高度优先事项是使其基础模型和想法在科学和工程领域都能被访问。人们越来越感兴趣的是一种基于概率推理的完全不同形式的类脑逻辑，这种逻辑在处理日益增加的“大数据”量所带来的搜索和识别问题时更加有效和节能。概率逻辑目前是用在确定性计算平台上运行的软件算法来实现的。其目标是为使用不稳定多铁性的新P（robbery）计算平台奠定基础，不稳定多铁性是一种结合了不同电气，机械和磁性的人造材料组合。与量子计算机不同，P计算机应该在室温下稳定运行，同时提供“量子并行性”的某些方面，以促进解决困难问题。该团队将致力于这种新颖的概率计算思想的材料，设备和电路开发。专家组将同时探讨与新材料特性的特性、各种结构参数的影响和建筑方面有关的技术工作。

项目成果

Hybrid spin-CMOS stochastic spiking neuron for high-speed emulation of In vivo neuron dynamics

• DOI: 10.1049/iet-cdt.2017.0145
• 发表时间: 2018-02
• 期刊: IET Comput. Digit. Tech.
• 作者: Steven D. Pyle;Kerem Y Çamsarı;R. Demara

Composable Probabilistic Inference Networks Using MRAM-based Stochastic Neurons

使用基于 MRAM 的随机神经元的可组合概率推理网络

• 发表时间: 2019
• 期刊: ACM journal on emerging technologies in computing systems
• 影响因子: 2.2
• 作者: Zand, Ramtin;Camsari, kerem Y.;Datta, Supriyo;DeMara, Ronald F.
• 通讯作者: DeMara, Ronald F.

ApGAN: Approximate GAN for Robust Low Energy Learning From Imprecise Components

• DOI: 10.1109/tc.2019.2949042
• 发表时间: 2020-03-01
• 期刊: IEEE TRANSACTIONS ON COMPUTERS
• 影响因子: 3.7
• 作者: Roohi, Arman;Sheikhfaal, Shadi;DeMara, Ronald F.
• 通讯作者: DeMara, Ronald F.

Subthreshold Spintronic Stochastic Spiking Neural Networks With Probabilistic Hebbian Plasticity and Homeostasis

• DOI: 10.1109/jxcdc.2019.2911046
• 发表时间: 2019-04
• 期刊: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
• 影响因子: 2.4
• 作者: Steven D. Pyle;Ramtin Zand;Shadi Sheikhfaal;R. Demara

AQuRate: MRAM-based Stochastic Oscillator for Adaptive Quantization Rate Sampling of Spectrally Sparse Signals

AQuRate：基于 MRAM 的随机振荡器，用于频谱稀疏信号的自适应量化率采样

• 发表时间: 2019
• 期刊: ACM Great Lakes Symposium on VLSI
• 作者: Salehi, S.;Zand, R.;Zaeemzadeh, A.;Rahnavard, N.;DeMara, R. F.
• 通讯作者: DeMara, R. F.