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Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors
Su Yeon Jung1,2, Gwang Ya Kim1, Sejin Kim1
1Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea.
Summary
Neuromorphic sensory platforms offer energy-efficient, low-latency processing for real-time environmental data. This review explores near-sensor and in-sensor computing architectures for artificial sensory systems.
Area of Science:
- Neuroscience and Materials Science
- Artificial Intelligence and Sensor Technology
Background:
- Conventional artificial sensory systems face challenges with data transfer overhead and energy inefficiency.
- Neuromorphic platforms, inspired by biological systems, offer a novel solution for efficient sensory processing.
- The massive influx of real-time environmental data necessitates advancements in sensory computing.
Purpose of the Study:
- To comprehensively review the structural evolution and research trends of neuromorphic near-sensor and in-sensor computing.
- To analyze the fundamental physical mechanisms of artificial neurons and synapses.
- To discuss sensor operating principles and categorize neuromorphic systems for sensory applications.
Main Methods:
- Systematic analysis of physical mechanisms for artificial neurons and synapses.
- Discussion of optical, mechanical, and chemical sensor principles.
- Categorization of neuromorphic systems into near-sensor and in-sensor computing architectures.
- Review of research trends tailored to the five human senses.
Main Results:
- Neuromorphic near-sensor and in-sensor computing architectures are defined and analyzed.
- The review covers fundamental mechanisms and sensor types relevant to artificial senses.
- Current research trends in neuromorphic sensory computing are discussed for various applications.
- Domain-specific bottlenecks are identified for future development.
Conclusions:
- Neuromorphic computing presents a promising paradigm for energy-efficient, low-latency sensory data processing.
- Near-sensor and in-sensor architectures offer distinct advantages in data handling and integration.
- Strategic guidelines are provided for developing next-generation, fully integrated artificial cognitive systems.
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