The present disclosure relates to a computing apparatus, a method, an integrated circuit chip and an integrated circuit device for performing a neural network operation. The computing apparatus may be included in a combined processing apparatus. The combined processing apparatus may further include a general interconnection interface and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete calculation operations specified by users. The combined processing apparatus may further include a storage apparatus. The storage apparatus is respectively connected to the computing apparatus and other processing apparatus, and the storage apparatus is used for storing data of the computing apparatus and other processing apparatus. Solutions of the present disclosure may be widely applied to various floating-point data computations.

The present disclosure relates to a computing apparatus, a method, an integrated circuit chip and an integrated circuit device for performing a neural network operation. The computing apparatus may be included in a combined processing apparatus. The combined processing apparatus may further include a general interconnection interface and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete calculation operations specified by users. The combined processing apparatus may further include a storage apparatus. The storage apparatus is respectively connected to the computing apparatus and other processing apparatus, and the storage apparatus is used for storing data of the computing apparatus and other processing apparatus. Solutions of the present disclosure may be widely applied to various floating-point data computations.

A Physical Unclonable Function (PUF) based true random number generator (TRNG), a method for generating true random numbers, and an associated electronic device are provided. The PUF based TRNG may include a first obfuscation circuit, a cryptography circuit coupled to the first obfuscation circuit, and a second obfuscation circuit coupled to the cryptography circuit. The first obfuscation circuit obtains a first PUF value from a PUF pool of the electronic device, and performs a first obfuscation function on a preliminary seed based on the first PUF value to generate a final seed. The cryptography circuit utilizes the final seed as a key of a cryptography function to generate preliminary random numbers. The second obfuscation circuit obtains a second PUF value from the PUF pool, and performs a second obfuscation function on the preliminary random numbers based on the second PUF value to generate final random numbers.

A Physical Unclonable Function (PUF) based true random number generator (TRNG), a method for generating true random numbers, and an associated electronic device are provided. The PUF based TRNG may include a first obfuscation circuit, a cryptography circuit coupled to the first obfuscation circuit, and a second obfuscation circuit coupled to the cryptography circuit. The first obfuscation circuit obtains a first PUF value from a PUF pool of the electronic device, and performs a first obfuscation function on a preliminary seed based on the first PUF value to generate a final seed. The cryptography circuit utilizes the final seed as a key of a cryptography function to generate preliminary random numbers. The second obfuscation circuit obtains a second PUF value from the PUF pool, and performs a second obfuscation function on the preliminary random numbers based on the second PUF value to generate final random numbers.

Emulating floating point calculation using lower precision format calculations is described. An example of a processor includes a floating point unit (FPU) to provide a native floating point operation in a first precision format; and systolic array hardware including multiple data processing units, wherein the processor is to receive data for performance of a matrix multiplication operation in the first precision format; enable an emulated floating point multiplication operation using one or more values with a second precision format, the second precision format having a lower precision than the first precision format, the emulated floating point multiplication including operation of the systolic array hardware; and generate an emulated result for the matrix multiplication operation.

Emulating floating point calculation using lower precision format calculations is described. An example of a processor includes a floating point unit (FPU) to provide a native floating point operation in a first precision format; and systolic array hardware including multiple data processing units, wherein the processor is to receive data for performance of a matrix multiplication operation in the first precision format; enable an emulated floating point multiplication operation using one or more values with a second precision format, the second precision format having a lower precision than the first precision format, the emulated floating point multiplication including operation of the systolic array hardware; and generate an emulated result for the matrix multiplication operation.

Embodiments are directed to selecting a multiplication operation to be scheduled in a first stage of an execution schedule, the multiplication operation meeting a first condition of having no dependency. An addition/subtraction operation is selected to be scheduled in the first stage of the execution schedule responsive to meeting the first condition. A process is performed which includes selecting another multiplication operation to be scheduled in a next stage of the execution schedule responsive to meeting the first condition or a second condition, the second condition including having a dependency that is fulfilled by a previous stage. The process includes selecting another addition/subtraction operation to be scheduled in the next stage of the execution schedule responsive to meeting the first or second condition, and repeating the process until each operation has been scheduled in the execution schedule, where the execution schedule is configured for execution by an arithmetic logic unit.

Circuits and associated methods for processing two floating-point numbers (A, B) to generate a sum (A+B) of the two numbers and a difference (A-B) of the two numbers include calculating (806) a sum (|A|+|B|) of the absolute values of the two floating-point numbers, using a same-sign floating-point adder (1020), to produce a first result. The method further comprises calculating (808) a difference (|A|−|B|) of the absolute values to produce a second result. The sum (A+B) and the difference (A-B) are generated (810, 812) based on the first result (|A|+|B|), the second result (|A|−|B|), and the sign of each floating-point number.

Circuits and associated methods for processing two floating-point numbers (A, B) to generate a sum (A+B) of the two numbers and a difference (A-B) of the two numbers include calculating (806) a sum (|A|+|B|) of the absolute values of the two floating-point numbers, using a same-sign floating-point adder (1020), to produce a first result. The method further comprises calculating (808) a difference (|A|−|B|) of the absolute values to produce a second result. The sum (A+B) and the difference (A-B) are generated (810, 812) based on the first result (|A|+|B|), the second result (|A|−|B|), and the sign of each floating-point number.

Disclosed is a neural network accelerator including a first bit operator generating a first multiplication result by performing multiplication on first feature bits of input feature data and first weight bits of weight data, a second bit operator generating a second multiplication result by performing multiplication on second feature bits of the input feature data and second weight bits of the weight data, an adder generating an addition result by performing addition based on the first multiplication result and the second multiplication result, a shifter shifting a number of digits of the addition result depending on a shift value to generate a shifted addition result, and an accumulator generating output feature data based on the shifted addition result.