Low precision computers can be efficient at finding possible answers to search problems. However, sometimes the task demands finding better answers than a single low precision search. A computer system augments low precision computing with a small amount of high precision computing, to improve search quality with little additional computing.

A primary interval for convergence of at least one power series in a transcendental function is interpolated; while selecting a number of interpolation points for a truncated expansion of power series by a selected order of truncation. A function and at least one derivative of the function of the truncated expansion of the selected order of truncation is evaluated at the interpolation points by computing a set of scaled values for convergence boundaries of the truncated expansion for a total number of values to be stored in the table; for each index value up to a value of the number of the interpolation points, evaluating the function and the derivative of the function of the truncated expansion converging in the set of scaled values to compute separate constant values for the primary function and each at least one derivative; and for each index value, adding each separate constant value to the table with a separate index value. Each separate value evaluated for the function and each derivative is saved in a table, wherein the table is looked up for efficiently computing a result of the truncated expansion of the at least one power series.

A processor to facilitate execution of a single-precision floating point operation on an operand is disclosed. The processor includes one or more execution units, each having a plurality of floating point units to execute one or more instructions to perform the single-precision floating point operation on the operand, including performing a floating point operation on an exponent component of the operand; and performing a floating point operation on a mantissa component of the operand, comprising dividing the mantissa component into a first sub-component and a second sub-component, determining a result of the floating point operation for the first sub-component and determining a result of the floating point operation for the second sub-component, and returning a result of the floating point operation.

Neural networks, in many cases, include convolution layers that are configured to perform many convolution operations that require multiplication and addition operations. Compared with performing multiplication on integer, fixed-point, or floating-point format values, performing multiplication on logarithmic format values is straightforward and energy efficient as the exponents are simply added. However, performing addition on logarithmic format values is more complex. Conventionally, addition is performed by converting the logarithmic format values to integers, computing the sum, and then converting the sum back into the logarithmic format. Instead, logarithmic format values may be added by decomposing the exponents into separate quotient and remainder components, sorting the quotient components based on the remainder components, summing the sorted quotient components using an asynchronous accumulator to produce partial sums, and multiplying the partial sums by the remainder components to produce a sum. The sum may then be converted back into the logarithmic format.

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.

Low precision computers can be efficient at finding possible answers to search problems. However, sometimes the task demands finding better answers than a single low precision search. A computer system augments low precision computing with a small amount of high precision computing, to improve search quality with little additional computing.

One embodiment provides a unified multifunction circuitry. The unified multifunction circuitry includes a logarithm circuitry and an antilogarithm circuitry. The logarithm circuitry is to determine a log output operand. The log output operand includes a piecewise linear approximation of a base 2 logarithm of a significand of a log input operand. The antilogarithm circuitry is to determine an antilog output operand. The antilog output operand includes a piecewise linear approximation of a base 2 antilogarithm of a fraction of a selected input operand.

A primary interval for convergence of at least one power series in a transcendental function is interpolated, while selecting a number of one or more interpolation points for a truncated expansion of the at least one power series by a selected order of truncation. A function and at least one derivative of the function of the truncated expansion of the selected order of truncation is evaluated at the one or more interpolation points. Each separate value evaluated for the function and each of the at least one derivative is saved in a table, wherein the table is looked up for efficiently computing a result of the truncated expansion of the at least one power series.

Low precision computers can be efficient at finding possible answers to search problems. However, sometimes the task demands finding better answers than a single low precision search. A computer system augments low precision computing with a small amount of high precision computing, to improve search quality with little additional computing.

Neural networks, in many cases, include convolution layers that are configured to perform many convolution operations that require multiplication and addition operations. Compared with performing multiplication on integer, fixed-point, or floating-point format values, performing multiplication on logarithmic format values is straightforward and energy efficient as the exponents are simply added. However, performing addition on logarithmic format values is more complex. Conventionally, addition is performed by converting the logarithmic format values to integers, computing the sum, and then converting the sum back into the logarithmic format. Instead, logarithmic format values may be added by decomposing the exponents into separate quotient and remainder components, sorting the quotient components based on the remainder components, summing the sorted quotient components using an asynchronous accumulator to produce partial sums, and multiplying the partial sums by the remainder components to produce a sum. The sum may then be converted back into the logarithmic format.