Methods and systems for division operation are described. A processor can initialize an estimated quotient between the dividend and the divisor separately from a floating-point unit (FPU) pipeline. The processor can implement the FPU pipeline to execute a refinement process that can include at least a first iteration of operations and a second iteration of operations. The refinement process can include, in the first iteration of operations, generating a first unnormalized floating-point value using the initialized estimated quotient. The refinement process can include, in the second iteration of operations, generating a second unnormalized floating-point value using the first unnormalized floating-point value. The processor can determine a final quotient based on the second unnormalized floating-point value.

A processor-implemented data processing method includes: normalizing input data of an activation function comprising a division operation; determining dividend data corresponding to a dividend of the division operation by reading, from a memory, a value of a first lookup table addressed by the normalized input data; determining divisor data corresponding to a divisor of the division operation by accumulating the dividend data; and determining output data of the activation function corresponding to an output of the division operation obtained by reading, from the memory, a value of a second lookup table addressed by the dividend data and the divisor data.

A computing device and related products are provided. The computing device is configured to perform machine learning calculations. The computing device includes an operation unit, a controller unit, and a storage unit. The storage unit includes a data input/output (I/O) unit, a register, and a cache. Technical solution provided by the present disclosure has advantages of fast calculation speed and energy saving.

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.

A computing device and related products are provided. The computing device is configured to perform machine learning calculations. The computing device includes an operation unit, a controller unit, and a storage unit. The storage unit includes a data input/output (I/O) unit, a register, and a cache. Technical solution provided by the present disclosure has advantages of fast calculation speed and energy saving.

A processor (and method) includes a core that performs a floating point division through execution of various instructions. The instructions include a sign, exponent, and mantissa (SEM) separation instruction which causes the core to extract the sign, exponent and mantissa values from numerator and denominator floating point numbers. The instructions also include an unsigned mantissa division instruction which cause the core to iteratively perform a conditional subtraction operation to compute a value indicative of a mantissa of the quotient. The instructions further include a merge instruction that causes the core to generate a quotient floating point number using the extracted sign and exponent from the SEM separation instruction and the value indicative of the mantissa of the quotient.

A binary logic circuit for determining the ratio x/d where x is a variable integer input, the binary logic circuit comprising: a logarithmic tree of modulo units each configured to calculate x[a: b] mod d for respective block positions a and b in x where b>a with the numbering of block positions increasing from the most significant bit of x up to the least significant bit of x, the modulo units being arranged such that a subset of M1 modulo units of the logarithmic tree provide x[0: m] mod d for all m{1, M}, and, on the basis that any given modulo unit introduces a delay of 1: all of the modulo units are arranged in the logarithmic tree within a delay envelope of log.sub.2 M; and more than M2.sup.u of the subset of modulo units are arranged at the maximal delay of log.sub.2 M, where 2.sup.u is the power of 2 immediately smaller than M.

Circuitry comprises ray tracing circuitry comprising a plurality of floating-point circuitries to perform floating-point processing operations to detect intersection between a virtual ray defined by a ray direction and a test region, the floating-point circuitries operating to a given precision to generate an output floating-point value comprising a significand and an exponent; in which at least some of the plurality of floating-point circuitries are configured to round using a predetermined directed rounding mode any denormal floating-point value generated by operation of that circuitry so as to output normal values, a denormal floating-point value being a floating-point value in which the significand comprises one or more leading zeroes.

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.

Techniques are disclosed relating to polynomial approximation of the base-2 logarithm. In some embodiments, floating-point circuitry is configured to perform an approximation of a base-2 logarithm operation and provide a fixed unit of least precision (ULP) error over a range of inputs. In some embodiments, the floating-point circuitry includes a set of parallel pipelines for polynomial approximation, where the output is chosen from a particular pipeline based on a determination of whether the input operand is in a first subset of a range of inputs. Disclosed techniques may advantageously provide fixed ULP error for an entire input operand range for the floating-point base-2 logarithmic function with minimal area and energy footprint, relative to traditional techniques.