First AI Chip That Learns and Infers
By Robin Mitchell, AI | 28-10-2025
A significant barrier to progress in artificial intelligence is physical, not algorithmic. Current approaches require vast amounts of specialized silicon, massive energy consumption, and complicated cooling systems.
This approach is unsustainable and could lead to serious resource shortages if AI continues to scale at its current pace. Data centers already strain power grids, and the heat generated requires industrial-scale cooling, often supported by water-intensive processes.
Training remains the most energy-intensive AI process, highlighting the need for efficient, adaptive edge computing solutions, such as merging FeCAPs and memristors.
AI continues to scale at its current pace, could see serious resource shortages.
Author's summary: AI chip learns and infers efficiently.
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