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

贝叶斯网络是一种在存在不确定性的情况下非常有效的计算模型。它在预测股市行为、疾病进展等任务中表现出色。它非常适合于处理已发生的事件，并预测不同已知原因的可能性。例如，给定患者的症状，它可以计算可能导致这些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，这些硬件昂贵、容易故障、消耗太多能量和消耗太多计算机芯片上的面积。这项研究的目标是通过用磁性设备取代传统的电子硬件来克服这些缺点，磁性设备以一种特殊的方式相互作用，从而得出贝叶斯推理。这可以显著降低硬件复杂性和所有相关成本，使贝叶斯网络变得紧凑而高效。这项研究将通过广泛的模拟来确定这种方法的可行性。研究生将接受这一领域的培训，以培养出一批具有尖端知识的熟练科学家和工程师。在存在不确定性的情况下进行计算的贝叶斯网络利用通过复杂硬件实现的贝叶斯推理引擎，这些硬件通常涉及微控制器、移位寄存器、模数转换器、逻辑门等，这些硬件消耗过多的能量，并在芯片上占用大量空间。项目组最近已经证明，通过偶极耦合相互作用的磁性隧道结(MTJ)可以实现极大地降低能源成本和更小的占地面积的贝叶斯网络。两个偶极耦合的MTJ A和B可以实现非常有效的2节点贝叶斯网络，其中MTJ A的高阻态和低阻态的概率由电流或电压来设置，而MTJ B的(随机)电阻状态由两个MTJ之间偶极耦合的不同程度来决定。偶极子耦合的程度是通过施加在MTJ B软层上的局部应变来调节的。这允许人们在两个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的推理机在不同场景下的生存能力，并设计最优的子系统。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.