Neuromorphic computing aims to emulate the brain’s computational architecture with low energy consumption, offering a way to enhance the performance of modern, power-hungry AI systems. Achieving brain-like cognition and maximizing neuromorphic benefits requires complex neurons, dense connectivity, heterogeneous compute components, scalability with trillions of learnable parameters, and novel algorithms. This necessitates a codesign approach spanning algorithms, architectures, systems, circuits, and devices, along with heterogeneous integration techniques. There is a significant opportunity to design efficient next-generation AI that leverages underlying hardware, like biological codesign seen in the brain.
