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Reducing Edge AI Power Demands: Hardware-Software Co-Design for Autonomous Systems
Recent breakthroughs in hardware-software co-design are slashing the energy cost of local AI inference, enabling sophisticated automation on off-grid power budgets.
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Efficiency Benchmarks for In-Memory Computing: Decoupling Local AI from the Power Grid
A global shift toward in-memory computing (IMC) is reducing the power requirements for edge AI inference by up to 90%, fundamentally altering the energy math for off-grid autonomous systems.
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