EAGER: Collaborative Research: Bayesian Reasoning Machine on a Magneto-Tunneling Junction Network

EAGER：协作研究：磁隧道结网络上的贝叶斯推理机

• 批准号: 2001255
• 负责人: Supriyo Bandyopadhyay
• 金额: $ 10万
• 依托单位: Virginia Commonwealth University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001255&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Bayesian; Reasoning

Bayesian networks are a computational model that is very efficient for computing in the presence of uncertainty. It excels in such tasks as predicting stock market behavior, disease progression, etc. It is ideal for taking an event that occurred and predicting the likelihood of different known causes to have been the contributing factor. For example, given the symptoms of a patient, it can compute the probabilities of various diseases that could be causing the symptoms. Unfortunately, implementing Bayesian networks usually requires complex hardware that is expensive, prone to failure, dissipates too much energy and consumes too much area on a computer chip. The goal of this research is to overcome these disadvantages by replacing traditional electronic hardware with magnetic devices that interact with each other in a special way to elicit Bayesian inference. This can reduce the hardware complexity and all associated costs dramatically, making Bayesian networks compact and efficient. This research will establish the viability of this approach through extensive simulations. Graduate students will be trained in this field to produce a pool of skilled scientists and engineers with cutting-edge knowledge.Bayesian networks for computing in the presence of uncertainty leverage Bayesian inference engines implemented with complex hardware that often involves microcontrollers, shift registers, analog-to-digital converters, logic gates, etc. that dissipate exorbitant amounts of energy and have enormous footprints on a chip. It ha recently been shown by the project team that magnetic tunnel junctions (MTJs) that interact with each other by means of dipole coupling can implement Bayesian networks with vastly reduced energy cost and much smaller footprints. Two dipole coupled MTJs A and B can realize an extremely efficient 2-node Bayesian network, where the probabilities of high and low resistance states of MTJ A are set by current or voltage, while the (random) resistance state of MTJ B is determined by varying degrees of dipole coupling between the two MTJs. The degree of dipole coupling is tuned with local strain applied to the soft layer of MTJ B using electrical excitation. This allows one to generate any desired anti-correlation or correlation between the resistance states of the two MTJs that can be varied between 0% and 100% using electrical excitation. In turn, this allows the generation of programmable conditional probabilities that can be exploited for Bayesian networks. This research will build a simulation base for this approach, test the viability of MTJ-based inference engines under different scenarios and design optimal sub-systems.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.

贝叶斯网络是一种计算模型，在不确定性存在下对于计算非常有效。它在预测股票市场行为，疾病进步等任务中表现出色。它是发生发生事件并预测不同已知原因的可能性的理想选择。例如，考虑到患者的症状，它可以计算可能导致症状的各种疾病的概率。不幸的是，实施贝叶斯网络通常需要昂贵的复杂硬件，容易发生故障，消散了过多的能量，并且在计算机芯片上消耗了太多的区域。这项研究的目的是通过用磁性设备替换传统的电子硬件来克服这些缺点，这些磁性设备以一种特殊的方式相互作用以引起贝叶斯推断。这可以大大降低硬件的复杂性和所有相关成本，从而使贝叶斯网络紧凑而有效。这项研究将通过广泛的模拟确定这种方法的可行性。研究生将在该领域接受培训，以生产具有尖端知识的熟练科学家和工程师。在存在不确定性的杠杆贝叶斯贝叶斯推理引擎的情况下进行计算的bayesian网络，这些网络通常涉及经常涉及微控制器，移动型登记册，类似物到数字化的脚步，逻辑转换器，等等的能量耗散量和耗尽量的数量来构成一定的e and量和一定数量。该项目团队最近显示，通过偶极耦合相互交互的磁性隧道连接（MTJ）可以实现贝叶斯网络，其能源成本却大大降低了，而且足迹较小。 两个偶极子偶联的MTJS A和B可以实现一个极其有效的2节点贝叶斯网络，其中MTJ A的高和低电阻状态的概率是由电流或电压设置的，而MTJ B的（随机）电阻状态由不同的偶极子在两个MTJ之间确定。使用电激发，用局部应变涂在MTJ B的软层上，调谐偶极子耦合的程度。这允许使用电激发在0％至100％之间变化的两个MTJ的电阻状态之间产生任何所需的抗相关性或相关性。反过来，这允许生成可以利用贝叶斯网络的可编程条件概率。这项研究将为这种方法建立一个模拟基础，测试基于MTJ的推理引擎在不同方案和设计最佳子系系统下的可行性。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来进行评估。

项目成果

An observable effect of spin inertia in slow magneto-dynamics: Increase of the switching error rates in nanoscale ferromagnets

慢磁动力学中自旋惯性的可观察效应：纳米级铁磁体中开关错误率的增加

• DOI: 10.1088/1361-648x/ac0cb4
• 发表时间: 2021
• 期刊: Journal of Physics: Condensed Matter
• 作者: Rahman, Rahnuma;Bandyopadhyay, Supriyo
• 通讯作者: Bandyopadhyay, Supriyo

The Cost of Energy-Efficiency in Digital Hardware: The Trade-Off between Energy Dissipation, Energy–Delay Product and Reliability in Electronic, Magnetic and Optical Binary Switches

数字硬件的能源效率成本：电子、磁性和光学二进制开关的能量耗散、能量延迟乘积与可靠性之间的权衡

• DOI: 10.3390/app11125590
• 发表时间: 2021
• 期刊: Applied Sciences
• 作者: Rahman, Rahnuma;Bandyopadhyay, Supriyo
• 通讯作者: Bandyopadhyay, Supriyo

Bayesian reasoning machine on a magneto-tunneling junction network

磁隧道连接网络上的贝叶斯推理机

• DOI: 10.1088/1361-6528/abae97
• 发表时间: 2020
• 期刊: Nanotechnology
• 影响因子: 3.5
• 作者: Nasrin, Shamma;Drobitch, Justine;Shukla, Priyesh;Tulabandhula, Theja;Bandyopadhyay, Supriyo;Trivedi, Amit Ranjan
• 通讯作者: Trivedi, Amit Ranjan

Robustness of Binary Stochastic Neurons Implemented With Low Barrier Nanomagnets Made of Dilute Magnetic Semiconductors

用稀磁半导体制成的低势垒纳米磁体实现二元随机神经元的鲁棒性

• DOI: 10.1109/lmag.2022.3202135
• 发表时间: 2022
• 期刊: IEEE Magnetics Letters
• 影响因子: 1.2
• 作者: Rahman, Rahnuma;Bandyopadhyay, Supriyo
• 通讯作者: Bandyopadhyay, Supriyo