Brain-inspired computing paradigms to improve AI performance and sustainability

Hear from the authors of a Frontiers in Science lead article as they present a novel approach to AI hardware design—integrating neuromorphic systems processing capabilities and compute-in-memory (CIM) techniques—to overcome the limitations of modern computing hardware.

The article outlines a comprehensive roadmap for future AI-hardware research, emphasizing hardware–algorithm co-design to accelerate innovation across sectors such as healthcare, transportation, and robotics.

Join the authors and an expert panel to discuss emerging strategies—such as spiking neural networks (SNNs) and next generation AI chips—that could reduce data center energy use and enable real-time intelligence in compact, power-constrained systems. Potential applications include on-device medical diagnostics, autonomous vehicles, and drones that navigate safely.