A method and apparatus are provided for manufacturing integrated circuits performing invariant integer division x/d. A desired rounding mode is provided and an integer triple (a,b,k) for this rounding mode is derived. Furthermore, a set of conditions for the rounding mode is derived. An RTL representation is then derived using the integer triple. From this a hardware layout can be derived and an integrated circuit manufactured with the derived hardware layout. When the integer triple is derived a minimum value of k for the desired rounding mode and set of conditions is also derived.

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 product-sum operation device includes a product operator and a sum operator. The product operator includes a plurality of product operation elements, and an alternative element that, when any of the plurality of product operation elements has malfunctioned, is used instead of the malfunctioning product operation element. Each of the plurality of product operation elements and the alternative element is a resistance change element. The sum operator includes an output detector which detects a sum of outputs from the plurality of product operation elements when the alternative element is not used.

A decimal floating-point instruction is executed in a round-for-reround mode. The decimal floating-point instruction is configured to perform a decimal floating-point operation on a decimal floating-point operand. The executing includes forming based on performing the decimal floating-point operation, an intermediate result having a high order portion and a low order portion. The high order portion has a least significant digit. A rounded-for-reround number is created from the intermediate result. The rounded-for-reround number includes the high order portion of the intermediate result and based on the least significant coefficient digit of the high order portion being a selected value and based on the low order portion having another selected value, the least significant digit of the rounded-for-reround number is incremented. The rounded-for-reround number is stored.

A processor or other device, such as a programmable and/or massively parallel processor or other device, includes processing elements designed to perform arithmetic operations (possibly but not necessarily including, for example, one or more of addition, multiplication, subtraction, and division) on numerical values of low precision but high dynamic range (“LPHDR arithmetic”). Such a processor or other device may, for example, be implemented on a single chip. Whether or not implemented on a single chip, the number of LPHDR arithmetic elements in the processor or other device in certain embodiments of the present invention significantly exceeds (e.g., by at least 20 more than three times) the number of arithmetic elements, if any, in the processor or other device which are designed to perform high dynamic range arithmetic of traditional precision (such as 32 bit or 64 bit floating point arithmetic).

A processor or other device, such as a programmable and/or massively parallel processor or other device, includes processing elements designed to perform arithmetic operations (possibly but not necessarily including, for example, one or more of addition, multiplication, subtraction, and division) on numerical values of low precision but high dynamic range (“LPHDR arithmetic”). Such a processor or other device may, for example, be implemented on a single chip. Whether or not implemented on a single chip, the number of LPHDR arithmetic elements in the processor or other device in certain embodiments of the present invention significantly exceeds (e.g., by at least 20 more than three times) the number of arithmetic elements, if any, in the processor or other device which are designed to perform high dynamic range arithmetic of traditional precision (such as 32 bit or 64 bit floating point arithmetic).

According to one embodiment, a calculating device includes a nonlinear oscillator. The nonlinear oscillator includes a circuit part including a first Josephson junction and a second Josephson junction, and a conductive member including a first terminal. An electrical signal is input to the first terminal. The electrical signal includes a first signal in a first operation. The first signal includes a first frequency component having a first frequency, and a second frequency component having a second frequency. The first frequency is 2 times an oscillation frequency of the nonlinear oscillator. An absolute value of a difference between the first frequency and the second frequency is not more than 0.3 times the first frequency.

A circuit includes a plurality of first counting gates, a first ternary half adder (THA) and a second THA that are connected to the plurality of first counting gates, a third THA configured to receive a sum output signal of the first THA and a sum output signal of the second THA, a first ternary sum gate configured to receive a carry output signal of the first THA and a carry output signal of the second THA, and a second ternary sum gate configured to receive a carry output signal of the third THA and an output signal of the first ternary sum gate, wherein the third THA and the second ternary sum gate may be configured to output voltage signals corresponding to a number of drain voltages among input signals applied to the plurality of first counting gates.

A superconducting circuit and a quantum computer capable of implementing four-body interaction while reducing an amount of hardware are provided. A superconducting circuit (1) includes four superconducting qubit circuits (10), a coupling circuit (20) inductively coupled to the four superconducting qubit circuits (10). Each of the superconducting qubit circuits (10) indicates a qubit by being in a first phase state or a second phase state, when the number of the superconducting qubit circuits (10) in the first phase state among the four superconducting qubit circuits (10) is an even number, an interaction term of Hamiltonian of the superconducting circuit (1) takes a first value, and when the number of the superconducting qubit circuits (10) in the first phase state among the four superconducting qubit circuits (10) is an odd number, the interaction term takes a second value.

A set of entries in a branch prediction structure for a set of second blocks are accessed based on a first address of a first block. The set of second blocks correspond to outcomes of one or more first branch instructions in the first block. Speculative prediction of outcomes of second branch instructions in the second blocks is initiated based on the entries in the branch prediction structure. State associated with the speculative prediction is selectively flushed based on types of the branch instructions. In some cases, the branch predictor can be accessed using an address of a previous block or a current block. State associated with the speculative prediction is selectively flushed from the ahead branch prediction, and prediction of outcomes of branch instructions in one of the second blocks is selectively initiated using non-ahead accessing, based on the types of the one or more branch instructions.