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Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

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NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of...
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The Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Atomic Nuclei: Nuclear Spin State Population Distribution01:14

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Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
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Atomic Nuclei: Nuclear Spin01:08

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All atomic particles possess an intrinsic angular momentum, or 'spin'. Electrons, protons, and neutrons each have a spin value of ½, although protons and neutrons in nuclei may have higher half-integer spins owing to energetic factors.
Atomic nuclei have a net nuclear spin, , which can have an integer or half-integer value. In atomic nuclei, the spins of protons are paired against each other but not with neutrons, and vice versa. Consequently, an even number of protons does not...
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Spin–Spin Coupling Constant: Overview01:08

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In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
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Related Experiment Video

Updated: Sep 19, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Quantized artificial neural networks implemented with spintronic stochastic computing.

Saadi Sabyasachi1, Walid Al Misba1, Yixin Shao2

  • 1Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA 23284, United States of America.

Nanotechnology
|June 8, 2025
PubMed
Summary
This summary is machine-generated.

Quantized stochastic computing (SC) using stochastic-magnetic tunnel junctions (s-MTJs) significantly reduces energy consumption and latency in artificial neural networks (ANNs) while maintaining high accuracy. This approach optimizes resource-intensive matrix vector multiplications for efficient hardware implementation.

Keywords:
deep neural networksmagnetic tunnel junctions (MTJ)quantizationstochastic computing

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Area of Science:

  • * Computer Science
  • * Electrical Engineering
  • * Materials Science

Background:

  • * Artificial neural network (ANN) inference demands substantial energy and device resources due to matrix vector multiplications.
  • * Stochastic computing (SC) offers a promising, less resource-intensive alternative for ANNs, utilizing random number generators (RNGs).
  • * Stochastic-magnetic tunnel junctions (s-MTJs) can generate random bitstreams for hardware-based SC, but prior work focused on analog weights.

Purpose of the Study:

  • * To investigate the efficacy of SC for matrix vector multiplication with quantized synaptic weights and outputs.
  • * To evaluate the performance of a quantized SC-ANN using experimental s-MTJ bitstreams and discrete weight/node states.
  • * To compare the energy consumption, latency, and accuracy of quantized SC-ANNs against analog implementations.

Main Methods:

  • * Implemented quantized SC-ANNs with 5 and 11 discrete states for weights and hidden layer nodes.
  • * Utilized experimentally obtained s-MTJ bitstreams with varying lengths (100-500 bits).
  • * Trained and performed inference on the MNIST dataset using neural networks with one and three hidden layers.

Main Results:

  • * Quantized SC-ANNs demonstrated reduced latency (9×) and energy consumption (2.6×) compared to analog s-MTJ ANNs.
  • * Training with SC consistently improved accuracy across all configurations.
  • * Peak accuracy of 96.82% was achieved with a 400-bit stochastic bitstream and three hidden layers.

Conclusions:

  • * Quantized SC-ANNs effectively reduce hardware resource requirements and improve energy efficiency.
  • * The use of discrete quantized states in SC-ANNs preserves accuracy while enhancing performance.
  • * This approach offers a viable path for energy-efficient, high-performance ANNs implemented with s-MTJs.