Neuromorphic Learning

Researchers have been working on silicon chips that can directly integrate neural network design as a result of the popularity of Deep Learning concepts that rely on neuron-based models. At the hardware level, these chips are programmed to simulate the human brain. In a typical chip, data must typically be transported between the central processing unit and storage blocks, which uses energy and adds time overhead. In a neuromorphic semiconductor, data is assembled and stored analogously, and it can create synapses as needed to conserve time and energy.

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