The circuit, resembling a neural network, learns tasks like nonlinear regression, operates at low power, and is trainable without a computer, showcasing potential for fast, low-power computing.
The learning circuitry's scalability and parallel updating of elements allow for efficient training irrespective of network size, with quick inference and low power requirements, demonstrating potential for various applications.
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