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The massive influx of continuous, real-time environmental data demands highly energy-efficient and low-latency sensory processing. Conventional artificial sensory systems are limited by severe data transfer overhead issues due to physically separated processing and memory units, coupled with analog-to-digital converters. To resolve these issues, neuromorphic sensory platforms inspired by the biological nervous system have emerged as an innovative paradigm. This Review comprehensively investigates the structural evolution and current research trends of neuromorphic near-sensor and in-sensor computing systems. Initially, the fundamental physical mechanisms underlying artificial neurons and synapses are systematically analyzed. Furthermore, the distinct operating principles of optical, mechanical, and chemical sensors corresponding to the five human senses are discussed. To establish a clear structural framework, we systematically categorize neuromorphic-integrated sensory systems into near-sensor and in-sensor computing architectures based on their level of integration. Near-sensor processing minimizes data movement through system-level integration, whereas in-sensor computing executes stimulus transduction and state evolution simultaneously at the device level. Based on this classification, we extensively discuss recent research trends of near-sensor and in-sensor computing tailored to each of the five human senses. Ultimately, by identifying domain-specific bottlenecks, this article provides strategic material and architectural guidelines for realizing fully integrated, next-generation artificial cognitive systems.
Jung et al. (Thu,) studied this question.