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

贝叶斯网络是一种计算模型，在存在不确定性的情况下非常有效。它擅长预测股票市场行为，疾病进展等任务，它是理想的采取发生的事件，并预测不同的已知原因的可能性已成为促成因素。例如，给定患者的症状，它可以计算可能导致症状的各种疾病的概率。不幸的是，实现贝叶斯网络通常需要复杂的硬件，这些硬件价格昂贵，容易出现故障，消耗太多的能量，并且在计算机芯片上消耗太多的面积。本研究的目标是克服这些缺点，用磁性设备取代传统的电子硬件，以一种特殊的方式相互作用，以引出贝叶斯推理。这可以大大降低硬件复杂性和所有相关成本，使贝叶斯网络紧凑而高效。这项研究将通过广泛的模拟来确定这种方法的可行性。研究生将接受该领域的培训，培养出一批具有尖端知识的熟练科学家和工程师。贝叶斯网络在不确定性存在的情况下利用贝叶斯推理引擎进行计算，这些推理引擎通常涉及微控制器，移位寄存器，模数转换器，逻辑门等复杂硬件，这些硬件消耗了大量的能量，并且在芯片上有巨大的足迹。项目团队最近表明，通过偶极耦合相互作用的磁性隧道结（MTJ）可以实现贝叶斯网络，大大降低能源成本和更小的足迹。 两个偶极耦合的MTJ A和B可实现极其有效的2节点贝叶斯网络，其中MTJ A的高电阻状态和低电阻状态的概率由电流或电压设定，而MTJ B的（随机）电阻状态由两个MTJ之间的偶极耦合的变化程度确定。偶极耦合的程度通过使用电激励施加到MTJ B的软层的局部应变来调谐。这允许在两个MTJ的电阻状态之间产生任何期望的反相关或相关，其可以使用电激励在0%和100%之间变化。反过来，这允许生成可用于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的推理引擎在不同场景下的可行性，并设计最佳的子系统。该奖项反映了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。

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.