The recent trend in adapting ultra-energy-efficient (but error-prone) nanomagnetic devices to non-Boolean computing and information processing (e.g. stochastic/probabilistic computing, neuromorphic, belief networks, etc) has resulted in rapid strides in new computing modalities. Of particular interest are Bayesian networks (BN) which may see revolutionary advances when adapted to a specific type of nanomagnetic devices. Here, we develop a novel nanomagnet-based computing substrate for BN that allows high-speed sampling from an arbitrary Bayesian graph. We show that magneto-tunneling junctions (MTJs) can be used for electrically programmable 'sub-nanosecond' probability sample generation by co-optimizing voltage-controlled magnetic anisotropy and spin transfer torque. We also discuss that just by engineering local magnetostriction in the soft layers of MTJs, one can stochastically couple them for programmable conditional sample generation as well. This obviates the need for extensive energy-inefficient hardware like OP-AMPS, gates, shift-registers, etc to generate the correlations. Based on the above findings, we present an architectural design and computation flow of the MTJ network to map an arbitrary Bayesian graph where we develop circuits to program and induce switching and interactions among MTJs. Our discussed framework can lead to a new generation of stochastic computing hardware for various other computing models, such as stochastic programming and Bayesian deep learning. This can spawn a novel genre of ultra-energy-efficient, extremely powerful computing paradigms, which is a transformational advance.

中文翻译：

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磁隧道结网络上的贝叶斯推理机

最近将超节能（但容易出错）纳米磁性设备应用于非布尔计算和信息处理（例如随机/概率计算、神经形态、信念网络等）的趋势导致新计算模式的快速发展。特别令人感兴趣的是贝叶斯网络 (BN)，当它适用于特定类型的纳米磁性设备时，它可能会看到革命性的进步。在这里，我们为 BN 开发了一种新型的基于纳米磁铁的计算基板，它允许从任意贝叶斯图中进行高速采样。我们表明，磁隧道结 (MTJ) 可通过共同优化电压控制磁各向异性和自旋转移扭矩来用于电可编程“亚纳秒”概率样本生成。我们还讨论了仅通过在 MTJ 的软层中设计局部磁致伸缩，人们也可以将它们随机耦合以进行可编程条件样本生成。这消除了对大量低能效硬件（如 OP-AMPS、门、移位寄存器等）的需要来生成相关性。基于上述发现，我们提出了 MTJ 网络的架构设计和计算流程，以映射任意贝叶斯图，我们在其中开发电路来编程和诱导 MTJ 之间的切换和交互。我们讨论的框架可以为各种其他计算模型带来新一代的随机计算硬件，例如随机编程和贝叶斯深度学习。这可以催生一种新型的超节能、极其强大的计算范式，这是一种变革性的进步。