An overview of brain-like computing: Architecture, applications, and future trends

Area of Science:

• Neuroscience
• Computer Science
• Artificial Intelligence

Background:

• Moore's Law is nearing its limit, necessitating novel computing paradigms.
• Understanding the human brain's computational principles is crucial for developing advanced AI.
• Current brain-like computing models aim to replicate specific brain functions for technological advancement.

Purpose of the Study:

• To provide a comprehensive overview of the progress and current state of brain-like computing.
• To analyze and compare existing brain-like computing models and hardware.
• To discuss the challenges and future directions in neuromorphic engineering applications.

Main Methods:

• Literature review and synthesis of current research in brain-like computing.
• Analysis of established and emerging brain-like computing models.
• Comparative study of mature brain-like computing chips and their applications.

Main Results:

• Summary of generally accepted and feasible brain-like computing models.
• Introduction and comparative analysis of leading brain-like computing chips.
• Outline of current application attempts and associated challenges in neuromorphic systems.

Conclusions:

• Brain-like computing is a promising field addressing the limitations of traditional computing.
• Further research is needed to overcome challenges in mimicking brain complexity and functionality.
• This review serves as a guide for researchers and practitioners in neuromorphic engineering.