Related Experiment Video
Updated: Jun 12, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Electronic hardware implementations of neural networks.
Applied Optics
|June 5, 2010
Summary
This study reviews electronic neural network hardware development, focusing on JPL
Area of Science:
- Computer Engineering
- Materials Science
- Artificial Intelligence
Background:
- Intense research focuses on the computational potential of highly parallel systems.
- Development of specialized hardware is crucial for advancing neural network research.
Purpose of the Study:
- To review current advancements in electronic neural network hardware.
- To highlight investigations at JPL on novel neural network hardware implementations.
Main Methods:
- Review of existing research on neural network hardware.
- Examination of JPL's work on custom very large scale integrated (VLSI) technology.
- Analysis of novel thin film materials and analog-digital hybrid architectures.
Main Results:
- JPL is actively developing neural network hardware using VLSI, thin film materials, and hybrid architectures.
- These hardware developments promise significant enhancements for parallel computing research.
Conclusions:
- The development of specialized neural network hardware is essential for unlocking the full potential of parallel computing systems.
- Advancements in VLSI, materials science, and hybrid architectures are key to future progress in this field.
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