What is it?
Neuromorphic computing is an emerging computing paradigm that takes inspiration from the structure and function of biological neural networks to design efficient and intelligent computing systems. It involves the development of artificial neural networks and specialized hardware architectures that mimic the way the human brain processes information. Neuromorphic systems are designed to emulate the highly parallel, distributed, and energy-efficient processing capabilities of biological brains, using artificial neurons and synapses as building blocks.
Why is it important?
Neuromorphic computing has the potential to be highly energy-efficient compared to traditional computing architectures, as it processes information in a massively parallel and event-driven manner, similar to the human brain. Neuromorphic systems can process and respond to data in real-time, enabling fast and efficient decision-making in dynamic environments, such as autonomous vehicles or industrial control systems. The distributed and parallel nature of neuromorphic architectures makes them highly scalable and resistant to faults or failures, as the system can adapt and compensate for local disruptions or errors.
What industries will it impact?
Artificial intelligence and machine learning, Robotics and autonomous systems, Edge computing and Internet of Things (IoT), Healthcare and biomedical devices, Aerospace and defense.
Market players
IBM, Syntiant, Expedera, SynSense, Celestial AI, Aspinity, Lux Semiconductors, NOVI, Microsoft.