A model building server and model building method thereof are provided. The model building server stores a model building program having a configuration combination. The model building server randomly generates a plurality of first configuration combination codes for feature categories, model algorithm categories and hyperparameters to set the configuration combination, and runs the model building program based on a first optimization algorithm to determine a first model. According to at least one determined feature category and at least one determined model algorithm category indicated by the configuration combination code corresponding to the first model, the model building server randomly generates a plurality of second configuration combination codes for features, model algorithms and hyperparameters to set the configuration combination, and runs the model building program based on a second optimization algorithm to determine an optimization model.

A method of increasing an efficiency at which a plurality of threshold gates arranged as neuromorphic hardware is able to perform a linear algebraic calculation having a dominant size of N. The computer-implemented method includes using the plurality of threshold gates to perform the linear algebraic calculation in a manner that is simultaneously efficient and at a near constant depth. Efficient is defined as a calculation algorithm that uses fewer of the plurality of threshold gates than a nave algorithm. The nave algorithm is a straightforward algorithm for solving the linear algebraic calculation. Constant depth is defined as an algorithm that has an execution time that is independent of a size of an input to the linear algebraic calculation. The near constant depth comprises a computing depth equal to or between O(log(log(N)) and the constant depth.

A data processing apparatus and method of operating a data processing apparatus are disclosed. Comparisons are made between first and second floating-point operands received. A more significant portion of the first floating-point operand and of the second floating-point operand are subject to comparison. The more significant portion of the first floating-point operand minus a least significant bit in the more significant portion is subject to comparison with the more significant portion of the second floating-point operand. A less significant portion of the first floating-point operand and of the second floating-point operand are also subject to comparison. In dependence on the outcome of these comparisons, right-shift circuitry is used selectively to perform a 1-bit right shift on a difference calculated between the first floating-point operand and the second floating-point operand.

A model building server and model building method thereof are provided. The model building server stores a model building program having a configuration combination. The model building server randomly generates a plurality of first configuration combination codes for feature categories, model algorithm categories and hyperparameters to set the configuration combination, and runs the model building program based on a first optimization algorithm to determine a first model. According to at least one determined feature category and at least one determined model algorithm category indicated by the configuration combination code corresponding to the first model, the model building server randomly generates a plurality of second configuration combination codes for features, model algorithms and hyperparameters to set the configuration combination, and runs the model building program based on a second optimization algorithm to determine an optimization model.

A method of increasing an efficiency at which a plurality of threshold gates arranged as neuromorphic hardware is able to perform a linear algebraic calculation having a dominant size of N. The computer-implemented method includes using the plurality of threshold gates to perform the linear algebraic calculation in a manner that is simultaneously efficient and at a near constant depth. Efficient is defined as a calculation algorithm that uses fewer of the plurality of threshold gates than a nave algorithm. The nave algorithm is a straightforward algorithm for solving the linear algebraic calculation. Constant depth is defined as an algorithm that has an execution time that is independent of a size of an input to the linear algebraic calculation. The near constant depth comprises a computing depth equal to or between O(log(log(N)) and the constant depth.