Apparatuses, systems, and techniques to generate numbers. In at least one embodiment, one or more circuits are to cause one or more thirty-two bit floating point numbers to be truncated to generate one or more rounded numbers based, at least in part, on one or more rounding attributes.

Numeric operations on a series of values, each encoded as a logarithm, may be performed. A first offset is determined based on values of an initial subset of the series. Numerical operations on individual elements of the series may then be performed by performing an exponentiation of a difference between the individual elements and the first offset. A particular element in the series may be identified as being unable to be included without causing an overflow or underflow of an exponentiation or numerical operation. In this event, a second offset may be determined. The numerical operations may then proceed by adjusting a current accumulated result using the second offset and continuing to perform numerical operations on individual elements of the series by performing an exponentiation of a difference between the individual elements and the second offset. Additional offsets may be optionally determined until the numerical operation is complete.

An apparatus to facilitate a computer number format conversion is disclosed. The apparatus comprises a control unit to receive to receive data format information indicating a first precision data format that input data is to be received and converter hardware to receive the input data and convert the first precision data format to a second precision data format based on the data format information.

An apparatus to facilitate a computer number format conversion is disclosed. The apparatus comprises a control unit to receive to receive data format information indicating a first precision data format that input data is to be received and converter hardware to receive the input data and convert the first precision data format to a second precision data format based on the data format information.

An electronic control device is provided with a plurality of calculation blocks that calculate floating-point data. The electronic control device includes a storage that stores calculation data items of the plurality of calculation blocks. The electronic control device calculates a command value of a control amount in a control target based on the calculation data items of the plurality of calculation blocks.

An apparatus comprises floating-point processing circuitry to perform a floating-point operation with rounding to generate a floating-point result value; and tininess detection circuitry to detect a tininess status indicating whether an outcome of the floating-point operation is tiny. A tiny outcome corresponds to a non-zero number with a magnitude smaller than a minimum non-zero magnitude representable as a normal floating-point number in a floating-point format to be used for the floating-point result value. The tininess detection circuitry comprises hardware circuit logic configured to support both before rounding tininess detection and after rounding tininess detection for detecting the tininess status.

An aspect includes executing, by a binary based floating-point arithmetic unit of a processor, a calculation having two or more operands in hexadecimal format based on a hexadecimal floating-point (HFP) instruction and providing a condition code for a calculation result of the calculation. The floating-point arithmetic unit includes a condition code anticipator circuit that is configured to provide the condition code to the processor prior to availability of the calculation result.

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.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.