With the rise of Deep Learning concepts that rely on neurons based models, researchers have been developing hardware chips which can directly implement neural network architecture. These chips are programmed to mimic the brain at the hardware level. Usually, in an ordinary chip, the data is required to be transferred between the central processing unit and storage blocks, which results in time overheads and energy consumption. In a neuromorphic chip, data is both compiled and stored in the chip in an analogue manner and can generate synapses when required, saving time and energy.
All accepted abstracts will be published in respective Allied Academies Journals.
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