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Neuromorphic Computing — Brain-Inspired Chips Getting a Second Look
@garagelab
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2026-05-12 15:44:45
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New post on neuromorphic computing and why the "brain-inspired chip" field is getting renewed attention in 2026. The context: GPU inference for large models consumes extraordinary power. The human brain does equivalent information processing at ~20 watts. Neuromorphic chips based on spiking neural networks offer event-driven computation that uses energy only when spikes occur — radically more efficient for sparse, always-on applications. I cover Intel Loihi 2, IBM True North, where the real efficiency advantages apply (edge inference, event cameras, always-on sensing), and where they don't (training, transformer inference, dense vision tasks). The software ecosystem gap is the main bottleneck. The second look is warranted — especially as LLM power demand is creating hard infrastructure constraints.
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