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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Probabilistic Neural Computing with Stochastic Devices.

Shashank Misra¹, Leslie C Bland², Suma G Cardwell³

¹Microsystems Engineering, Science and Applications, Sandia National Laboratories, Albuquerque, NM, 87123, USA.

Advanced Materials (Deerfield Beach, Fla.)

|November 17, 2022

Summary

This summary is machine-generated.

Neuromorphic computing, inspired by the brain, can be enhanced by its stochasticity for probabilistic applications. This research proposes a co-design approach for scalable hardware to advance scientific computing and artificial intelligence.

Keywords:

magnetic tunnel junctions neuromorphic computing probabilistic computing stochastic computing tunnel diodes

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Area of Science:

Neuroscience and Computer Science
Neuromorphic Engineering
Probabilistic Computing

Background:

The brain's architecture inspires computing systems with integrated memory, event-driven communication, and analog computation.
Neuromorphic systems enhance efficiency and speed in scientific computing and artificial intelligence.
Current probabilistic computing efforts often focus on single scales of the microelectronics stack.

Purpose of the Study:

To propose leveraging the brain's stochasticity as inspiration for expanding neuromorphic computing to probabilistic applications.
To introduce a co-design vision for integrating stochastic devices into scalable neuromorphic architectures.
To present a framework for categorizing hardware-based probabilistic computing technologies.

Main Methods:

Exploring the integration of stochastic devices (e.g., magnetic tunnel junctions, tunnel diodes) into neuromorphic architectures.
Developing a co-design strategy for operating large numbers of devices in a stochastic regime.
Presenting a categorization framework for probabilistic computing hardware.

Main Results:

Demonstrating the potential of stochastic neuromorphic systems for probabilistic applications.
Highlighting the scalability of the proposed co-design approach.
Providing a structured overview of emerging probabilistic computing hardware.

Conclusions:

The stochastic nature of the brain offers a novel pathway for advancing neuromorphic computing.
A co-design approach integrating stochastic devices is crucial for scalable probabilistic neuromorphic systems.
This work lays the groundwork for future developments in hardware-based probabilistic computing for AI and scientific simulations.