Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions

• URL: http://arxiv.org/abs/2504.08257v1
• Date: Fri, 11 Apr 2025 05:02:27 GMT
• Title: Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions
• Authors: Yingqian Xu, Xiaohan Li, Caihua Wan, Ran Zhang, Bin He, Shiqiang Liu, Jihao Xia, Dehao Kong, Shilong Xiong, Guoqiang Yu, Xiufeng Han,
• Abstract summary: We present proof-of-concept experiments demonstrating the use of spin-orbit torque magnetic tunnel junctions (SOT-MTJs) in Bayesian network reasoning.<n>The parameters of the network can also approach the optimum through a simple point-by-point training algorithm.<n>We developed a simple medical diagnostic system using the SOT-MTJ as a random number generator and sampler.
• Score: 7.081096702778852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian networks play an increasingly important role in data mining, inference, and reasoning with the rapid development of artificial intelligence. In this paper, we present proof-of-concept experiments demonstrating the use of spin-orbit torque magnetic tunnel junctions (SOT-MTJs) in Bayesian network reasoning. Not only can the target probability distribution function (PDF) of a Bayesian network be precisely formulated by a conditional probability table as usual but also quantitatively parameterized by a probabilistic forward propagating neuron network. Moreover, the parameters of the network can also approach the optimum through a simple point-by point training algorithm, by leveraging which we do not need to memorize all historical data nor statistically summarize conditional probabilities behind them, significantly improving storage efficiency and economizing data pretreatment. Furthermore, we developed a simple medical diagnostic system using the SOT-MTJ as a random number generator and sampler, showcasing the application of SOT-MTJ-based Bayesian reasoning. This SOT-MTJ-based Bayesian reasoning shows great promise in the field of artificial probabilistic neural network, broadening the scope of spintronic device applications and providing an efficient and low-storage solution for complex reasoning tasks.

Related papers

• On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks [92.66231524298554]
  iterative magnitude pruning (IMP) has become a popular method for extracting sparse convolutionalworks that can be trained to high performance.<n>Recent work showed that applying IMP to fully connected neural networks (FCNs) leads to the emergence of local receptive fields (RFs)<n>Inspired by results showing that training on synthetic images with highly non-Gaussian statistics (e.g., sharp edges) is sufficient to drive the emergence of local RFs in FCNs, we hypothesize that IMP iteratively increases the non-Gaussian statistics of FCN representations.
  arXiv  Detail & Related papers  (2024-12-09T14:56:23Z)
• MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle [0.0]
  This paper proposes a novel neural network architecture, the MEP-Net, which combines the MEP with neural networks to generate probability distributions from moment constraints. We show that the MEP-Net can be particularly useful in modeling the evolution of probability distributions in biochemical reaction networks and in generating complex distributions from data.
  arXiv  Detail & Related papers  (2024-12-03T02:21:29Z)
• Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data [0.0]
  We introduce fully Bayesian Neural Networks (FBNNs) for active learning tasks in the'small data' regime. FBNNs provide reliable predictive distributions, crucial for making informed decisions under uncertainty in the active learning setting. Here, we assess the suitability and performance of FBNNs with the No-U-Turn Sampler for active learning tasks in the'small data' regime.
  arXiv  Detail & Related papers  (2024-05-16T05:20:47Z)
• Tractable Function-Space Variational Inference in Bayesian Neural Networks [72.97620734290139]
  A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters. We propose a scalable function-space variational inference method that allows incorporating prior information. We show that the proposed method leads to state-of-the-art uncertainty estimation and predictive performance on a range of prediction tasks.
  arXiv  Detail & Related papers  (2023-12-28T18:33:26Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Probabilistic Verification of ReLU Neural Networks via Characteristic Functions [11.489187712465325]
  We use ideas from probability theory in the frequency domain to provide probabilistic verification guarantees for ReLU neural networks. We interpret a (deep) feedforward neural network as a discrete dynamical system over a finite horizon. We obtain the corresponding cumulative distribution function of the output set, which can be used to check if the network is performing as expected.
  arXiv  Detail & Related papers  (2022-12-03T05:53:57Z)
• Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling [19.518237361775533]
  In BNNs, a complete posterior distribution of the unknown weight and bias parameters of the network is produced during the training stage. This feature is useful in countless machine learning applications. It is particularly appealing in areas where decision-making has a crucial impact, such as medical healthcare or autonomous driving.
  arXiv  Detail & Related papers  (2022-10-03T14:59:23Z)
• FF-NSL: Feed-Forward Neural-Symbolic Learner [70.978007919101]
  This paper introduces a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FF-NSL) FF-NSL integrates state-of-the-art ILP systems based on the Answer Set semantics, with neural networks, in order to learn interpretable hypotheses from labelled unstructured data.
  arXiv  Detail & Related papers  (2021-06-24T15:38:34Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)
• Multi-fidelity Bayesian Neural Networks: Algorithms and Applications [0.0]
  We propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity. We apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs)
  arXiv  Detail & Related papers  (2020-12-19T02:03:53Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.