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Thermal Management in Neuromorphic Materials, Devices, and Networks
Felipe Torres1,2, Ali C Basaran3, Ivan K Schuller3
1Physics Department, Faculty of Science, University of Chile, 653, Santiago, 7800024, Chile.
Advanced Materials (Deerfield Beach, Fla.)
|September 6, 2022
Summary
Neuromorphic computing, inspired by the brain, offers energy-efficient artificial intelligence. This review explores thermal management strategies for advanced neuromorphic devices, crucial for sustainable AI development.
Area of Science:
- Computer Science
- Neuroscience
- Materials Science
Background:
- Machine learning, or artificial intelligence (AI), is rapidly advancing, mimicking biological neural networks.
- Current AI relies on von Neumann architecture, which is energy-inefficient and poses thermal challenges.
- Neuromorphic computing, a brain-inspired paradigm, offers a path toward more efficient hardware.
Purpose of the Study:
- To review the current state of thermal management in neuromorphic computing technology.
- To identify challenges and opportunities for energy-efficient neuromorphic device implementation.
- To explore the integration of brain-inspired principles and advanced materials for computing.
Main Methods:
- Description of brain-inspired computing principles and quantum materials for neuromorphic devices.
- Discussion of brain criticality and resistive switching-based neuromorphic devices.
- Analysis of energy and electrical considerations for spiking-based computation and brain thermal regulation.
Main Results:
- Physical mechanisms for thermal management and thermoelectric control in neuromorphic devices are analyzed.
- Key features of brain thermal regulation are addressed in the context of computing.
- Challenges and new avenues for energy-efficient computing implementation are identified.
Conclusions:
- Effective thermal management is critical for the practical implementation of energy-efficient neuromorphic computing.
- Integrating principles from neuroscience and materials science, such as quantum materials, is essential.
- Further research into thermoelectric control and brain-inspired architectures can unlock the full potential of sustainable AI.
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