Neuromorphic and in-memory computing architectures

Neuromorphic and in-memory computing architectures combine brain-inspired processing with memory-centric computation to overcome the power, latency, and data-movement limitations of traditional systems. Neuromorphic processors use spiking neural networks and event-driven operation to enable ultra-low-power, real-time intelligence, while in-memory computing performs computation directly within memory arrays to accelerate AI workloads. These architectures are critical for edge AI, autonomous systems, and next-generation AI accelerators, delivering higher performance per watt, lower latency, and scalable intelligence. Strong industry investment, emerging standards, and expanding patent activity are driving commercialization across semiconductor, automotive, and embedded AI markets.