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1 results about "Axon" patented technology

An axon (from Greek ἄξων áxōn, axis), or nerve fiber, is a long, slender projection of a nerve cell, or neuron, in vertebrates, that typically conducts electrical impulses known as action potentials away from the nerve cell body. The function of the axon is to transmit information to different neurons, muscles, and glands. In certain sensory neurons (pseudounipolar neurons), such as those for touch and warmth, the axons are called afferent nerve fibers and the electrical impulse travels along these from the periphery to the cell body, and from the cell body to the spinal cord along another branch of the same axon. Axon dysfunction has caused many inherited and acquired neurological disorders which can affect both the peripheral and central neurons. Nerve fibers are classed into three types – group A nerve fibers, group B nerve fibers, and group C nerve fibers. Groups A and B are myelinated, and group C are unmyelinated. These groups include both sensory fibers and motor fibers. Another classification groups only the sensory fibers as Type I, Type II, Type III, and Type IV.

Neuromorphic calculation circuit based on multi-bit parallel binary synaptic array

ActiveCN110378475AReduce power consumptionReduce areaAnalogue/digital conversionElectric signal transmission systemsIntegratorHigh energy
The invention discloses a neuromorphic calculation circuit based on a multi-bit parallel binary synapse array. The neuromorphic calculation circuit comprises a neural axon module, the multi-bit parallel binary RRAM synapse array, a time division multiplexer, a plurality of integrators and a shared successive approximation analog-to-digital converter, wherein the neural axon module comprises two basic units, namely a time sequence scheduler and an adder, and the time sequence scheduler is used for arranging the time sequence of signals, so that input signals are sequentially input into a multi-bit parallel binary RRAM synapse array by adopting a dendritic priority strategy; the adder is used for expanding the array scale, and when the configured neural network input layer is greater than the input of one RRAM array, the adder is used for adding the calculation results of the plurality of arrays to obtain the output of the network layer. Compared with the current system, the method has the advantages of high precision and low power consumption, can be configured into most deep neural network applications, and is particularly suitable for being deployed in edge computing equipment with high energy consumption requirements.
Owner:ZHEJIANG UNIV
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