First AI Chip That Learns and Infers
The first AI chip that learns and infers has been developed, combining FeCAPs and memristors for efficient, adaptive edge computing.
According to Robin Mitchell, one of the major obstacles to progress in artificial intelligence is physical, not algorithmic. Current AI systems require large amounts of specialized silicon, consume massive amounts of energy, and need complicated cooling systems, which is unsustainable.
The current approach to AI has significant environmental and economic implications, including straining power grids and generating large amounts of heat that require industrial-scale cooling, often using water-intensive processes.
Training AI models remains the most energy-intensive process, and if AI continues to scale at its current pace, it could lead to serious resource shortages.
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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