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Neural sampling machine with stochastic synapse allows brain-like learning and inference

Author

Listed:
  • Sourav Dutta

    (University of Notre Dame)

  • Georgios Detorakis

    (University of California Irvine)

  • Abhishek Khanna

    (University of Notre Dame)

  • Benjamin Grisafe

    (University of Notre Dame)

  • Emre Neftci

    (University of California Irvine)

  • Suman Datta

    (University of Notre Dame)

Abstract

Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Brain-inspired probabilistic models of neural network can explicitly handle the uncertainty in data and allow adaptive learning on the fly. However, their implementation in a compact, low-power hardware remains a challenge. In this work, we introduce a novel hardware fabric that can implement a new class of stochastic neural network called Neural Sampling Machine (NSM) by exploiting the stochasticity in the synaptic connections for approximate Bayesian inference. We experimentally demonstrate an in silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor (FeFET)-based analog weight cell with a two-terminal stochastic selector element. We show that the stochastic switching characteristic of the selector between the insulator and the metallic states resembles the multiplicative synaptic noise of the NSM. We perform network-level simulations to highlight the salient features offered by the stochastic NSM such as performing autonomous weight normalization for continual online learning and Bayesian inferencing. We show that the stochastic NSM can not only perform highly accurate image classification with 98.25% accuracy on standard MNIST dataset, but also estimate the uncertainty in prediction (measured in terms of the entropy of prediction) when the digits of the MNIST dataset are rotated. Building such a probabilistic hardware platform that can support neuroscience inspired models can enhance the learning and inference capability of the current artificial intelligence (AI).

Suggested Citation

  • Sourav Dutta & Georgios Detorakis & Abhishek Khanna & Benjamin Grisafe & Emre Neftci & Suman Datta, 2022. "Neural sampling machine with stochastic synapse allows brain-like learning and inference," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30305-8
    DOI: 10.1038/s41467-022-30305-8
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    References listed on IDEAS

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    1. Stefano Ambrogio & Pritish Narayanan & Hsinyu Tsai & Robert M. Shelby & Irem Boybat & Carmelo Nolfo & Severin Sidler & Massimo Giordano & Martina Bodini & Nathan C. P. Farinha & Benjamin Killeen & Chr, 2018. "Equivalent-accuracy accelerated neural-network training using analogue memory," Nature, Nature, vol. 558(7708), pages 60-67, June.
    2. Sourav Dutta & Abhinav Parihar & Abhishek Khanna & Jorge Gomez & Wriddhi Chakraborty & Matthew Jerry & Benjamin Grisafe & Arijit Raychowdhury & Suman Datta, 2019. "Programmable coupled oscillators for synchronized locomotion," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Lars Buesing & Johannes Bill & Bernhard Nessler & Wolfgang Maass, 2011. "Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-22, November.
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    1. Liuting Shan & Qizhen Chen & Rengjian Yu & Changsong Gao & Lujian Liu & Tailiang Guo & Huipeng Chen, 2023. "A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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