UNSUPERVISED LEARNING USING NEUROMORPHIC COMPUTING

    公开(公告)号:US20180174053A1

    公开(公告)日:2018-06-21

    申请号:US15385031

    申请日:2016-12-20

    Inventor: Tsung-Han Lin

    CPC classification number: G06N3/088 G06N3/049 G06N3/063

    Abstract: A spiking neural network (SNN) is implemented on a neuromorphic computers and includes a plurality of neurons, a first set of the plurality of synapses defining feed-forward connections from a first subset of the neurons to a second subset of the neurons, a second subset of the plurality of synapses to define recurrent connections between the second subset of neurons, and a third subset of the plurality of synapses to define feedback connections from the second subset of neurons to the first subset of neurons. A set of input vectors are provided to iteratively modify weight values of the plurality of synapses. Each iteration involves selectively enabling and disabling the third subset of synapses with a different one of the input vectors applied to the SNN. The weight values are iteratively adjusted to derive a solution to an equation comprising an unknown matrix variable and an unknown vector variable.

    Instruction and Logic for Nearest Neighbor Unit

    公开(公告)号:US20170091655A1

    公开(公告)日:2017-03-30

    申请号:US14865124

    申请日:2015-09-25

    CPC classification number: G06N20/00 G06F9/3836 G06F15/76

    Abstract: A processor includes a front end to decode an instruction, an allocator to pass the instruction to a nearest neighbor logic unit (NNLU) to execute the instruction, and a retirement unit to retire the instruction. The NNLU includes logic to determine input of the instruction for which nearest neighbors will be calculated, transform the input, retrieve candidate atoms for which the nearest neighbors will be calculated, compute distance between the candidate atoms and the input, and determine the nearest neighbors for the input based upon the computed distance.

Patent Agency Ranking