AI training happens in the cloud because it’s compute-intensive and highly parallel. It requires massive datasets, specialized hardware, and weeks of runtime. Inference, by contrast, is the deployment phase — smaller, faster, and often done at the edge, in real time. The cloud handles the heavy lifting; the edge delivers the result. Now, recent advances in resistive memory technology are making edge AI inferencing more energy-efficient, secure, and responsive.
At the 2025 IEEE Symposium on VLSI Technology and Circuits, researchers from CEA-Leti, Weebit Nano, and the Université Paris-Saclay presented a breakthrough in “on-chip customized learning” — demonstrating how a ReRAM-based platform can support few-shot learning using just five training updates.
A personalised tool to help users track selected stocks. Delivering real-time notifications on price updates, announcements, and performance stats on each to help make informed investment decisions.
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