Brain-Inspired Chips Could Slash AI Energy Use and Put LLMs on the Edge
What if a helpful language model could run all day on a smartwatch battery, or a data center could slash its AI power bill without sacrificing responsiveness? That promise is animating a surge of activity around brain-inspired chips. Neuromorphic processors fire only when events happen; memristor accelerators move math into memory arrays to avoid data shuttling. Recent papers show transformer-like workloads reimagined for event-driven execution, spiking attention mechanisms, and co-design strategies that train models to tolerate analog noise and drift. The remaining question is the one buyers care about: How do these systems stack up, end to end, against today’s GPUs and NPUs on energy per token, latency, and accuracy?
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