Neuromorphic computing: Bridging AI and electronics

Neuromorphic computing represents a transformative approach to integrating artificial intelligence (AI) with electronics, drawing inspiration from the human brain’s architecture. By designing chips with artificial neurons and synapses, such as Intel’s Loihi, neuromorphic systems enable energy-efficient, event-driven processing and real-time adaptability, unlike traditional CPUs and GPUs. These systems leverage spiking neural networks (SNNs) and innovations like Geoffrey Hinton’s Forward-Forward Algorithm to mirror biological learning, offering a synergy of hardware and software that enhances AI’s scalability in edge devices like wearables and IoT systems. This article explores how neuromorphic computing bridges theoretical AI with practical electronics, fostering applications in healthcare, transportation, and sustainable urban systems. By amplifying human capabilities rather than replacing them, neuromorphic computing redefines technology’s role, ensuring AI complements human creativity, morality, and purpose, paving the way for a symbiotic future where humanity remains central.