While much of the tech world remains fixated on the latest large language models (LLMs) powered by Nvidia GPUs, a quieter revolution is brewing in AI hardware. As the limitations and energy demands of traditional deep learning architectures become increasingly apparent, a new paradigm called neuromorphic computing is emerging – one that promises to slash the computational and power requirements of AI by orders of magnitude.

But what exactly are neuromorphic systems? To find out, VentureBeat spoke with Sumeet Kumar, CEO and founder of Innatera, a leading startup in the neuromorphic chip space.

 “Neuromorphic processors are designed to mimic the way biological brains process information,” Kumar explained. “Rather than performing sequential operations on data stored in memory, neuromorphic chips use networks of artificial neurons that communicate through spikes, much like real neurons.”
This brain-inspired architecture gives neuromorphic systems distinct advantages, particularly for edge computing applications in consumer devices and industrial IoT. Kumar highlighted several compelling use cases, including always-on audio processing for voice activation, real-time sensor fusion for robotics and autonomous systems, and ultra-low power computer vision.
“The key is that neuromorphic processors can perform complex AI tasks using a fraction of the energy of traditional solutions,” Kumar noted. “This enables capabilities like continuous environmental awareness in battery-powered devices that simply weren’t possible before.”

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