Hardware implementation of memristor-based artificial neural networks.

Area of Science:

• * Materials Science
• * Computer Engineering
• * Artificial Intelligence

Background:

• * Conventional von Neumann architectures face limitations in data communication bandwidth for data-intensive AI applications.
• * Emerging computing paradigms like near-memory computing offer partial solutions but a paradigm shift is needed.
• * Memristors, a novel beyond-CMOS technology, present a promising solution for efficient AI hardware.

Purpose of the Study:

• * To review the latest advancements in hardware-based memristive artificial neural networks (ANNs).
• * To detail the working principles, design alternatives, and associated challenges of memristive ANNs.
• * To provide a comprehensive protocol for researchers entering the field of memristive neural networks.

Main Methods:

• * Review of current research and development in memristive devices for AI hardware.
• * Detailed analysis of memristor device physics and integration into neural network architectures.
• * Discussion of design considerations, including parallelism, low-power operation, and performance metrics estimation.

Main Results:

• * Memristors enable in-memory computing, combining storage and processing to overcome the von Neumann bottleneck.
• * Hardware implementations of memristive ANNs show potential for significant gains in energy efficiency and computational throughput.
• * Practical challenges in fabrication, variability, and system integration require further research and development.

Conclusions:

• * Memristive technology is a key enabler for next-generation, energy-efficient artificial intelligence hardware.
• * A holistic understanding of materials, device design, and network architecture is crucial for realizing practical memristive ANNs.
• * This review serves as a guide for researchers and experts in the field of memristive neural networks.