Abstract: 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.

Abstract: A method, a computer system, and a computer program product for detection of unintended dependencies between hardware design signals from pseudo-random number generator (PRNG) taps is provided. Embodiments of the present invention may include identifying one or more tap points in a design as an execution sequence. Embodiments of the present invention may include sampling the tap points by propagating the tap points in the design with different delays. Embodiments of the present invention may include defining observation points to identify tap collisions based on the tap points. Embodiments of the present invention may include identifying tap collisions. Embodiments of the present invention may include identifying one or more sources of the tap collisions in the design. Embodiments of the present invention may include eliminating the one or more sources of uninteresting tap collisions out of the tap collisions and filtering one or more of the tap collisions.

Abstract: An arithmetic logic unit (ALU) including a binary, parallel adder and multiplier to perform arithmetic operations is described. The ALU includes an adder circuit coupled to a multiplexer to receive input operands that are directed to either an addition operation or a multiplication operation. During the multiplication operation, the ALU is configured to determine partial product operands based on first and second operands and provide the partial product operands to the adder circuit via the multiplexer, and the adder circuit is configured to provide an output having a value equal to a product of the first operand second operands. During an addition operation, the ALU is configured to provide the first and second operands to the adder circuit via the multiplexer, and the adder circuit is configured to provide the output having a value equal to a sum of the first and second operands.

Abstract: A calculation device includes a memory and one or more processors coupled to the memory and configured to alternately update, for elements each associated with first and second variables, the first and second variables, sequentially for unit times from an initial time to an end time. In an updating process for each unit time, the one or more processors are configured to: update, for each of the elements, the first variable based on the second variable; when the first variable is smaller than a first value, change the first variable to the first value and change the second variable to a third value; when the first variable is greater than a second value, change the first variable to the second value and change the second variable to the third value; and add an acceleration value calculated by a predetermined computation to the second variable.

Abstract: Methods, systems, and devices for split and duplicate ripple circuits are described. A ripple circuit may be divided into stages, which may operate in parallel. For example, a first stage may have a finite number of possibilities for an output that is relevant for a second stage, and the second stages may be replicated according to the finite number of possibilities. The replicated second stages thus may operate concurrently with each other and the first stage, with each of the replicated second stages assuming a different possible output from the first stage. Once operation of the first stage is complete, the true output of the first stage may be used to select one of the second stages as corresponding to the correct assumed output, and the output of the selected second stage may be or may be included in a set of output signals for the circuit.

Abstract: A device of executing a cryptographic operation on bit vectors, the execution of the cryptographic operation includes the execution of at least one arithmetic addition operation between a first operand and a second operand. Each operand comprises a set of components, each component corresponding to a given bit position of the operand. The device comprises a set of elementary adders, each elementary adder being associated with a given bit position of the operands and being configured to perform a bitwise addition between a component of the first operand at the given bit position and the corresponding component of the second operand at the given bit position using the carry generated by the computation performed by the elementary adder corresponding to the previous bit position. Each elementary adder has a sum output corresponding to the bitwise addition and a carry output, the result of the arithmetic addition operation being derived from the sum outputs provided by each elementary adder.

Abstract: An integrated circuit with specialized processing blocks is provided. A specialized processing block may be optimized for machine learning algorithms and may include a multiplier data path that feeds an adder data path. The multiplier data path may be decomposed into multiple partial product generators, multiple compressors, and multiple carry-propagate adders of a first precision. Results from the carry-propagate adders may be added using a floating-point adder of the first precision. Results from the floating-point adder may be optionally cast to a second precision that is higher or more accurate than the first precision. The adder data path may include an adder of the second precision that combines the results from the floating-point adder with zero, with a general-purpose input, or with other dot product terms. Operated in this way, the specialized processing block provides a technical improvement of greatly increasing the functional density for implementing machine learning algorithms.

Abstract: A calculation apparatus according to an embodiment includes matrix multiplication circuitry, time evolution circuitry, management circuitry, and output circuitry. The matrix multiplication circuitry calculates N second intermediate variables at a first time point by matrix multiplication between N (N>=2) first intermediate variables at the first time point and a preset coefficient matrix in N rows and N columns. The time evolution circuitry calculates N first variables at a second time point and N first intermediate variables at the second time point, the second time point being a time point following one sampling period after the first time point. The management circuitry increments time point from a start time point for each sampling period and controls the above circuitry to perform a process for each time point. The output circuitry outputs N first variables at a preset end time point.

Abstract: A logic circuit comprising: inputs for receiving multiple n-bit numbers, n being greater than one; and an adder capable of receiving m n-bit numbers, m being greater than one, and forming an output representing the sum of those numbers, the adder having a plurality of single-bit stages and being configured to form the sum by subjecting successive bits of each of the numbers to an operation in a respective one of the single-bit stages, the single-bit stages being such that the adder has insufficient capacity to add all possible combinations of bits in a respective bit position of m n-bit numbers; the addition circuit being configured to add the multiple n-bit numbers by: in the adder, adding a first one of the n-bit numbers to a value corresponding to a set of non-consecutive bits of another of the n-bit numbers to form a first intermediate value; adding the first intermediate value to a value corresponding to the bits of the said other of the n-bit numbers other than those in the said set to form a sum; and o

Abstract: Described herein are systems and methods that increase the utilization and performance of computational resources, such as storage space and computation time, thereby, reducing computational cost. Various embodiments of the invention provide for a hardware structure that allows both streaming of source data that eliminates redundant data transfer and allows for in-memory computations that eliminate requirements for data transfer to and from intermediate storage. In certain embodiments, computational cost is reduced by using a hardware structure that enables mathematical operations, such as element-wise matrix multiplications employed by convolutional neural networks, to be performed automatically and efficiently.

Abstract: An apparatus includes a processing circuit and a storage device. The processing circuit is configured to perform one or more processing operations in response to one or more instructions to generate an anchored-data element. The storage device is configured to store the anchored-data element. A format of the anchored-data element includes an identification item, an overlap item, and a data item. The data item is configured to hold a data value of the anchored-data element. The identification item indicates an anchor value for the data value or one or more special values.

Abstract: The present disclosure relates to a computing system. The computing system comprises a data input configured to receive an input data signal, a computation unit having an input coupled with the data input, the computation unit being operative to apply a weight to a signal received at its input to generate a weighted output signal, and a controller. The controller is configured to monitor a parameter of the input signal and/or a parameter of the output signal and to issue a control signal to the computation unit to control a level of accuracy of the weighted output signal based at least in part on the monitored parameter.

Abstract: An integrated circuit including a plurality of logarithmic addition-accumulator circuits, connected in series, to, in operation, perform logarithmic addition and accumulate operations, wherein each logarithmic addition-accumulator circuit includes: (i) a logarithmic addition circuit to add a first input data and a filter weight data, each having the logarithmic data format, and to generate and output first sum data having a logarithmic data format, and (ii) an accumulator, coupled to the logarithmic addition circuit of the associated logarithmic addition-accumulator circuit, to add a second input data and the first sum data output by the associated logarithmic addition circuit to generate first accumulation data. The integrated circuit may further include first data format conversion circuitry, coupled to the output of each logarithmic addition circuit, to convert the data format of the first sum data to a floating point data format wherein the accumulator may be a floating point type.

Abstract: Systems and methods for configuring a reduced instruction set computer processor architecture to execute fully homomorphic encryption (FHE) logic gates as a streaming topology. The method includes parsing sequential FHE logic gate code, transforming the FHE logic gate code into a set of code modules that each have in input and an output that is a function of the input and which do not pass control to other functions, creating a node wrapper around each code module, configuring at least one of the primary processing cores to implement the logic element equivalents of each element in a manner which operates in a streaming mode wherein data streams out of corresponding arithmetic logic units into the main memory and other ones of the plurality arithmetic logic units.

Abstract: A Bias Unit Element (UE) has a digital input, a sign input, and a chop clock. The sign input is exclusive ORed with the chop clock to generate a signed chop clock. Each Bias UE comprises a positive Bias UE and a negative Bias UE, each comprising groups of NAND gates generating an output and a complementary output, each of which are coupled to differential charge transfer lines through binary weighted charge transfer capacitors to a differential charge transfer bus comprising a positive charge transfer line and a negative charge transfer line. The chopped sign input enables the positive Bias UE when the sign bit is positive and enables the negative Bias UE when the sign bit is negative.

Abstract: A method for assigning a random number to a user in a set of users includes computing a random number assignment seed value based on an ASCII-value representation of the user's name, dividing the random number assignment seed value by a quantity of unassigned numbers available to be assigned to the user to produce a modified random number assignment seed value, rounding the modified random number assignment seed value down to an integer, computing a random number offset value by multiplying the quantity of unassigned numbers by the rounded modified random number assignment seed value, subtracting the random number assignment offset value from the random number assignment seed value to determine a random number assignment lookup number, determining the random number to be assigned to the user based on the random number assignment lookup number, and assigning the determined random number to the user.

Abstract: A method and system are provided for solving combinatorial optimization problems. A classical algorithm provides an approximate or “seed” solution which is then used by a quantum circuit to search its “neighborhood” for higher-quality feasible solutions. A continuous-time quantum walk (CTQW) is implemented on a weighted, undirected graph that connects the feasible solutions. An iterative optimizer tunes the quantum circuit parameters to maximize the probability of obtaining high-quality solutions from the final state. The ansatz circuit design ensures that only feasible solutions are obtained from the measurement. The disclosed method solves constrained problems without modifying their cost functions, confines the evolution of the quantum state to the feasible subspace, and does not rely on efficient indexing of the feasible solutions as some previous methods require.

Abstract: A neural network arithmetic processing device is capable of implementing a further increase in speed and efficiency of multiply-accumulate arithmetic operation, suppressing an increase in circuit scale, and performing multiply-accumulate arithmetic operation with simple design. A neural network arithmetic processing device includes a first multiply-accumulate arithmetic unit, a register connected to the first multiply-accumulate arithmetic unit, and a second multiply-accumulate arithmetic unit connected to the register. The first multiply-accumulate arithmetic unit has a first memory, a second memory, a first multiplier, a first adder, and a first output unit. The second multiply-accumulate arithmetic unit has an input unit, a third memory, second multipliers, second adders, and second output units.

Abstract: A system processing a stream of input data ordered row by row from a data array has a first integrated circuit (IC) adapted to apply an aperture function to the stream of input data, to produce an output data stream, and a second IC coupled to the first IC, the second IC adapted to manage context from row to row, retaining partial values as computed by the aperture function for each column along a row, and providing the partial values back to the aperture function for subsequent rows as needed to complete output values.

Abstract: According to one embodiment, an arithmetic device includes: a first input terminal; a second input terminal; an output terminal; a first logical shifter; a second logical shifter; a third logical shifter; a first AND gate; a second AND gate; a first multiplexer; a third AND gate; a first adder; a fourth logical shifter; a second multiplexer; a second adder; a first arithmetic shifter; a second arithmetic shifter; a third arithmetic shifter; a third multiplexer; a fourth multiplexer; and a fifth multiplexer.