An execution unit comprising a processing pipeline configured to perform calculations to evaluate a plurality of mathematical functions. The processing pipeline comprises a plurality of stages through which each calculation for evaluating a mathematical function progresses to an end result. Each of a plurality of processing circuits in the pipeline is configured to perform an operation on input values during at least one stage of the plurality of stages. The plurality of processing circuits include multiplier circuits. A first multiplier circuit and a second multiplier circuit are configured to operate in parallel, such that at the same stage in the processing pipeline, the first multiplier circuit and the second multiplier circuit perform their processing. A third multiplier circuit is arranged in series with the first multiplier circuit and the second multiplier circuit and processes outputs from the first multiplier circuit and the second multiplier circuit.

A method is provided that includes performing, by a processor in response to a vector sort instruction, sorting of values stored in lanes of the vector to generate a sorted vector, wherein the values in a first portion of the lanes are sorted in a first order indicated by the vector sort instruction and the values in a second portion of the lanes are sorted in a second order indicated by the vector sort instruction; and storing the sorted vector in a storage location.

The present disclosure advantageously provides a mixed precision computation (MPC) unit for executing one or more mixed-precision layers of an artificial neural network (ANN). The MPC unit includes a multiplier circuit configured to input a pair of operands and output a product, a first adder circuit coupled to the multiplier circuit, a second adder circuit, coupled to the first adder circuit, configured to input a pair of operands, an accumulator circuit, coupled to the multiplier circuit and the first adder circuit, configured to output an accumulated value, and a controller, coupled to the multiplier circuit, the first adder circuit, the second adder circuit and the accumulator circuit, configured to input a mode control signal. The controller has a plurality of operating modes including a high precision mode, a low precision add mode and a low precision multiply mode.

The present disclosure advantageously provides a mixed precision computation (MPC) unit for executing one or more mixed-precision layers of an artificial neural network (ANN). The MPC unit includes a multiplier circuit configured to input a pair of operands and output a product, a first adder circuit coupled to the multiplier circuit, a second adder circuit, coupled to the first adder circuit, configured to input a pair of operands, an accumulator circuit, coupled to the multiplier circuit and the first adder circuit, configured to output an accumulated value, and a controller, coupled to the multiplier circuit, the first adder circuit, the second adder circuit and the accumulator circuit, configured to input a mode control signal. The controller has a plurality of operating modes including a high precision mode, a low precision add mode and a low precision multiply mode.

Herein described is a method of operating an accumulation process in a data processing apparatus. The accumulation process comprises a plurality of accumulations which output a respective plurality of accumulated values, each based on a stored value and a computed value generated by a data processing operation. The method comprises storing a first accumulated value, the first accumulated value being one of said plurality of accumulated values, into a first storage device comprising a plurality of single-bit storage elements; determining that a predetermined trigger has been satisfied with respect to the accumulation process; and in response to the determining, storing at least a portion of a second accumulated value, the second accumulated value being one of said plurality of accumulated values, into a second storage device.

Examples described herein generally relate to performing conformance testing of a computational operation. A reference result including one or more reference intermediate products and a reference accumulator output at a first level of precision can be generated for the computational operation and based on one or more inputs. A hardware result can similarly be created using hardware at a second level of precision. The reference result can be compared to the hardware result to determine a variance value. A conformance result can be output based on whether the variance value is within a threshold range.

Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

Provided are an integrated circuit chip apparatus and a related product, the integrated circuit chip apparatus being used for executing a multiplication operation, a convolution operation or a training operation of a neural network. The present technical solution has the advantages of a small amount of calculation and low power consumption.

An instruction is executed in round-for-reround mode wherein the permissible resultant value that is closest to and no greater in magnitude than the infinitely precise result is selected. If the selected value is not exact and the units digit of the selected value is either 0 or 5, then the digit is incremented by one and the selected value is delivered. In all other cases, the selected value is delivered.

An instruction is executed in round-for-reround mode wherein the permissible resultant value that is closest to and no greater in magnitude than the infinitely precise result is selected. If the selected value is not exact and the units digit of the selected value is either 0 or 5, then the digit is incremented by one and the selected value is delivered. In all other cases, the selected value is delivered.