Automated transition from non-neuromorphic to neuromorphic processing

    公开(公告)号:US10346211B2

    公开(公告)日:2019-07-09

    申请号:US16039949

    申请日:2018-07-19

    摘要: An apparatus includes a processor to: assign a portion of currently available instruction-based processing resources to a first non-neuromorphic performance of an analytical function; in response to availability of sufficient remaining processing resources for a first neuromorphic performance of the analytical function with the same input values, assign a portion of the remaining processing resources to the first neuromorphic performance; analyze the output values generated by the first neuromorphic and non-neuromorphic performances to determine a degree of accuracy of the neural network in performing the analytical function; in response to at least the degree of accuracy exceeding a predetermined threshold, assign a portion of currently available processing resources to a second neuromorphic performance of the analytical function; and in response to availability of sufficient remaining processing resources for a second non-neuromorphic performance of the analytical function, assign a portion of the remaining instruction-based processing resources to the second non-neuromorphic performance.

    Distributed neuromorphic processing performance accountability

    公开(公告)号:US10338968B2

    公开(公告)日:2019-07-02

    申请号:US16039863

    申请日:2018-07-19

    摘要: An apparatus includes a processor to: receive a request to repeat an earlier performance of a first job flow described in a job flow definition; analyze the job flow definition to determine whether the first job flow uses a neural network; in response to a determination that the first job flow uses a neural network, analyze an object associated with the first job flow to determine whether the neural network was trained using training data from a second job flow that does not use a neural network; and in response to a determination that such training data was so used, repeat the earlier performance of the first job flow, perform the second job flow with the same input data values as used in the repeated performance of the first job flow, and analyze corresponding output data values of both performances to determine a degree of accuracy of the neural network.