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Lernende Memristoren lösen Navigationsaufgaben: «Das macht das System enorm schnell und effizient»

KI-Berechnungen auf herkömmlicher Hardware sind ineffizient und brauchen viel Energie. Nicht zuletzt deshalb wird vermehrt im Bereich des neuromorphen Rechnens geforscht, das sich am menschlichen…

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Sarah Schiller5mo agode

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SK hynix and TetraMem collaborate on experimental chip to bolster energy efficiency for edge AI devices — memristor-based in-memory SoC research leaves performance questions up in the air

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AI with Cerebellum-like Functions: A New 'Memtransistor' for Efficient Computing

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NeuEdge: A Neuromorphic Computing Framework for Energy-Efficient Edge AI with Adaptive Spiking Neural Networks

Edge AI applications increasingly require ultra-low-power, low-latency inference. Neuromorphic computing based on event-driven spiking neura

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